Tootfinder

The anatomy of a flash flood: Why the Texas flood was so deadly #Texas

The anatomy of a flash flood: Why the Texas flood was so deadly
Between July 3 and 6, Texas Hill Country experienced catastrophic flash flooding along the Guadalupe River system. The floods claimed at least 130 lives, with over 96 fatalities in Kerr County alone. More than 160 people were missing as of July 12, including children attending camps along the river.

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Jeremias Ferrao, Matthijs van der Lende, Ilija Lichkovski, Clement Neo
https://arxiv.org/abs/2509.12934

The Anatomy of Alignment: Decomposing Preference Optimization by Steering Sparse Features
Aligning large language models is critical for their usability and safety. However, the prevailing approach of Reinforcement Learning from Human Feedback (RLHF) induces diffuse, opaque parameter changes, making it difficult to discern what the model has internalized. Hence, we introduce Feature Steering with Reinforcement Learning (FSRL), a transparent alignment framework that trains a lightweight adapter to steer behavior by modulating interpretable features from a Sparse Autoencoder (SAE). Fi…

Multi Anatomy X-Ray Foundation Model
Nishank Singla, Krisztian Koos, Farzin Haddadpour, Amin Honarmandi Shandiz, Lovish Chum, Xiaojian Xu, Qing Jin, Erhan Bas
https://arxiv.org/abs/2509.12146

Multi Anatomy X-Ray Foundation Model
X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, …

Casey Muratori – The Big OOPs: Anatomy of a Thirty-five-year Mistake – BSC 2025 by Better Software Conference
#development

im judging books by the cover soooo hard rn i wanna read this even though i dont know shit abt it

The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
Sourish Dutta
https://arxiv.org/abs/2506.13936

The Anatomy of India's Industrial Interdependencies: 7-Digit Product-Level Supply-Use and Input-Output Tables from ASI Data (2016-2022) with a Case Study of the Mobile Phone Sector
Outlines the construction of high-resolution, 7-digit product-level Supply-Use Tables (SUTs) and symmetric Input-Output Tables (IOTs) for the Indian economy, leveraging microdata from the Annual Survey of Industries (ASI) for the period 2016-2022. We delineate a robust methodology encompassing the generation of detailed input and output flows, with a particular focus on the reconciliation of data from registered and unregistered manufacturing sectors through a meticulously developed NPCMS-NIC c…

Anatomy of Two Giant Deals: The U.A.E. Got Chips. The Trump Team Got Crypto Riches.
A lucrative transaction involving the Trump family’s cryptocurrency firm and an agreement giving the Emiratis access to A.I. chips were connected in ways that have not been previously reported.

In Giant Deals, U.A.E. Got Chips, and Trump Team Got Crypto Riches
A lucrative transaction involving the Trump family’s cryptocurrency firm and an agreement giving the Emiratis access to A.I. chips were connected in ways that have not been previously reported.

Anatomy of Two Giant Deals: The U.A.E. Got Chips. The Trump Team Got Crypto Riches: A lucrative transaction involving the Trump family’s cryptocurrency firm and an agreement giving the Emiratis access to A.I. chips were connected in ways that have not been previously reported.
In Giant Deals, U.A.E. Got Chips, and Trump Team Got Crypto Riches - The New York Times
https://archive.ph/DtDEo

U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT
Zhi Qin Tan, Xiatian Zhu, Owen Addison, Yunpeng Li
https://arxiv.org/abs/2509.12069 https://

U-Mamba2: Scaling State Space Models for Dental Anatomy Segmentation in CBCT
Cone-Beam Computed Tomography (CBCT) is a widely used 3D imaging technique in dentistry, providing volumetric information about the anatomical structures of jaws and teeth. Accurate segmentation of these anatomies is critical for clinical applications such as diagnosis and surgical planning, but remains time-consuming and challenging. In this paper, we present U-Mamba2, a new neural network architecture designed for multi-anatomy CBCT segmentation in the context of the ToothFairy3 challenge. U-…

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse
https://arxiv.org/abs/2508.11450

Subcortical Masks Generation in CT Images via Ensemble-Based Cross-Domain Label Transfer
Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of traumatic brain injuries and neurodegenerative disorders. However, training accurate automatic models requires large amounts of labelled data. Despite the availability of publicly available subcortical segmentation datasets for Magnetic Resonance Imaging (MRI), a significant gap exists for Computed Tomography (CT). This paper proposes an automatic ensemble …

from my link log —
Activity Monitor anatomy: memory accounting on Mac OS.
https://www.bazhenov.me/posts/activity-monitor-anatomy/
saved 2025-07-30

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
De-Xing Huang, Xiao-Hu Zhou, Mei-Jiang Gui, Xiao-Liang Xie, Shi-Qi Liu, Shuang-Yi Wang, Tian-Yu Xiang, Rui-Ze Ma, Nu-Fang Xiao, Zeng-Guang Hou
https://arxiv.org/abs/2508.10794

VasoMIM: Vascular Anatomy-Aware Masked Image Modeling for Vessel Segmentation
Accurate vessel segmentation in X-ray angiograms is crucial for numerous clinical applications. However, the scarcity of annotated data presents a significant challenge, which has driven the adoption of self-supervised learning (SSL) methods such as masked image modeling (MIM) to leverage large-scale unlabeled data for learning transferable representations. Unfortunately, conventional MIM often fails to capture vascular anatomy because of the severe class imbalance between vessel and background…

Efficient Image-to-Image Schr\"odinger Bridge for CT Field of View Extension
Zhenhao Li, Long Yang, Xiaojie Yin, Haijun Yu, Jiazhou Wang, Hongbin Han, Weigang Hu, Yixing Huang
https://arxiv.org/abs/2508.11211

Efficient Image-to-Image Schrödinger Bridge for CT Field of View Extension
Computed tomography (CT) is a cornerstone imaging modality for non-invasive, high-resolution visualization of internal anatomical structures. However, when the scanned object exceeds the scanner's field of view (FOV), projection data are truncated, resulting in incomplete reconstructions and pronounced artifacts near FOV boundaries. Conventional reconstruction algorithms struggle to recover accurate anatomy from such data, limiting clinical reliability. Deep learning approaches have been explor…

Anatomy of a mass disappearance in Mexico -- EL PAÍS English https://english.elpais.com/international/2025-06-28/anatomy-of-a-mass-disappearance-in-mexico-mom-i-fell-into-the-hands-of-the-mafia.htm…

Anatomy of a mass disappearance in Mexico: ‘Mom, I fell into the hands of the mafia’
EL PAÍS reconstructs the last movements of a group of 40 migrants from Cuba, Honduras and Ecuador who disappeared on December 21, 2024, in San José El Hueyate, on the coast of Chiapas. Six months later, no authorities have begun to search for them.

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Benjamin Laufer, Hamidah Oderinwale, Jon Kleinberg
https://arxiv.org/abs/2508.06811 https://

Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Many have observed that the development and deployment of generative machine learning (ML) and artificial intelligence (AI) models follow a distinctive pattern in which pre-trained models are adapted and fine-tuned for specific downstream tasks. However, there is limited empirical work that examines the structure of these interactions. This paper analyzes 1.86 million models on Hugging Face, a leading peer production platform for model development. Our study of model family trees -- networks th…

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Hanxue Gu, Yaqian Chen, Nicholas Konz, Qihang Li, Maciej A. Mazurowski
https://arxiv.org/abs/2507.11569

Are Vision Foundation Models Ready for Out-of-the-Box Medical Image Registration?
Foundation models, pre-trained on large image datasets and capable of capturing rich feature representations, have recently shown potential for zero-shot image registration. However, their performance has mostly been tested in the context of rigid or less complex structures, such as the brain or abdominal organs, and it remains unclear whether these models can handle more challenging, deformable anatomy. Breast MRI registration is particularly difficult due to significant anatomical variation b…

Anatomy of parameter-estimation biases in overlapping gravitational-wave signals: detector network
Ziming Wang, Dicong Liang, Lijing Shao
https://arxiv.org/abs/2509.07737 https:…

Anatomy of parameter-estimation biases in overlapping gravitational-wave signals: detector network
With the significantly improved sensitivity and a wider frequency band, the next-generation gravitational-wave (GW) detectors are anticipated to detect $\sim 10^5$ GW signals per year with durations from hours to days, leading to inevitable signal overlaps in the data stream. While a direct fitting for all signals is infeasible, extracting only one signal will be biased by its overlap with other signals. From this perspective, understanding how the biases arise from the overlapping and their de…

The anatomy of Green AI technologies: structure, evolution, and impact
Lorenzo Emer, Andrea Mina, Andrea Vandin
https://arxiv.org/abs/2509.10109 https://ar…

The anatomy of Green AI technologies: structure, evolution, and impact
Artificial intelligence (AI) is a key enabler of innovation against climate change. In this study, we investigate the intersection of AI and climate adaptation and mitigation technologies through patent analyses of a novel dataset of approximately 63 000 Green AI patents. We analyze patenting trends, corporate ownership of the technology, the geographical distributions of patents, their impact on follow-on inventions and their market value. We use topic modeling (BERTopic) to identify 16 major …

AnatomyCarve: A VR occlusion management technique for medical images based on segment-aware clipping
Andrey Titov, Tina N. H. Nantenaina, Marta Kersten-Oertel, Simon Drouin
https://arxiv.org/abs/2507.05572

AnatomyCarve: A VR occlusion management technique for medical images based on segment-aware clipping
Visualizing 3D medical images is challenging due to self-occlusion, where anatomical structures of interest can be obscured by surrounding tissues. Existing methods, such as slicing and interactive clipping, are limited in their ability to fully represent internal anatomy in context. In contrast, hand-drawn medical illustrations in anatomy books manage occlusion effectively by selectively removing portions based on tissue type, revealing 3D structures while preserving context. This paper introd…

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Katharina Beckh, Elisa Studeny, Sujan Sai Gannamaneni, Dario Antweiler, Stefan R\"uping
https://arxiv.org/abs/2507.01802

The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
Automatic medical coding has the potential to ease documentation and billing processes. For this task, transparency plays an important role for medical coders and regulatory bodies, which can be achieved using explainability methods. However, the evaluation of these approaches has been mostly limited to short text and binary settings due to a scarcity of annotated data. Recent efforts by Cheng et al. (2023) have introduced the MDACE dataset, which provides a valuable resource containing code ev…

Quantum Kinetic Anatomy of Electron Angular Momenta Edge Accumulation
T. Valet, H. Jaffres, V. Cros, R. Raimondi
https://arxiv.org/abs/2507.06771 https://

Quantum Kinetic Anatomy of Electron Angular Momenta Edge Accumulation
Controlling electron's spin and orbital degrees of freedom has been a major research focus over the past two decades, as it underpins the electrical manipulation of magnetization. Leveraging a recently introduced quantum kinetic theory of multiband systems [T. Valet and R. Raimondi, Phys. Rev. B 111, L041118 (2025)], we outline how the intrinsic angular momenta linear response is partitioned into intraband and interband contributions. Focusing on time reversal and inversion symmetric metals, we…

from my link log —
Anatomy of a SYN-ACK attack.
https://www.akamai.com/blog/security/anatomy-of-a-syn-ack-attack
saved 2025-07-22

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Jonathan Wang, Hisashi Ishida, David Usevitch, Kesavan Venkatesh, Yi Wang, Mehran Armand, Rachel Bronheim, Amit Jain, Adnan Munawar
https://arxiv.org/abs/2506.20496

Critical Anatomy-Preserving & Terrain-Augmenting Navigation (CAPTAiN): Application to Laminectomy Surgical Education
Surgical training remains a crucial milestone in modern medicine, with procedures such as laminectomy exemplifying the high risks involved. Laminectomy drilling requires precise manual control to mill bony tissue while preserving spinal segment integrity and avoiding breaches in the dura: the protective membrane surrounding the spinal cord. Despite unintended tears occurring in up to 11.3% of cases, no assistive tools are currently utilized to reduce this risk. Variability in patient anatomy fu…

Anatomy of Family Trees in Cosmological Correlators
Bingchu Fan, Zhong-Zhi Xianyu
https://arxiv.org/abs/2509.02684 https://arxiv.org/pdf/2509.02684

Anatomy of Family Trees in Cosmological Correlators
The time-ordered multilayer integrals have long been cited as major challenges in the analytical study of cosmological correlators and wavefunction coefficients. The recently proposed family tree decomposition technique solved these time integrals in terms of canonical objects called family trees, which are multivariate hypergeometric functions with energies as variables and twists as parameters. In this work, we provide a systematic study of the analytical properties of family trees. By exploi…

A new normal in America’s immigration courts:
Courthouse arrests have resulted many more detentions of undocumented immigrants without criminal records.
The arrests have turned courthouses into places to witness Trump’s immigration crackdown unfold in real time
-- every day.

Anatomy of an ICE Arrest
Migrants show up for hearings or scheduled check-ins. In growing numbers, they end up in handcuffs as President Trump’s immigration crackdown shifts tactics.

A 240 Elements Matrix Probe with Aberration Mask for 4D Carotid Artery Computational Ultrasound Imaging
Yuyang Hu, Michael Brown, Didem Dogan, Mah\'e Bulot, Maxime Cheppe, Guillaume Ferin, Geert Leus, Antonius F. W. van der Steen, Pieter Kruizinga, Johannes G. Bosch
https://arxiv.org/abs/2508.10527…

A 240 Elements Matrix Probe with Aberration Mask for 4D Carotid Artery Computational Ultrasound Imaging
Three-dimensional (3D) ultrasound provides enhanced visualization of the carotid artery (CA) anatomy and volumetric flow, offering improved accuracy for cardiovascular diagnosis and monitoring. However, fully populated matrix transducers with large apertures are complex and costly to implement. Computational ultrasound imaging (cUSi) offers a promising alternative by enabling simplified hardware design through model-based reconstruction and spatial field encoding. In this work, we present a 3D …

Weekend Reads
* FreeBSD Journal https://freebsdfoundation.org/our-work/journal/browser-based-edition/networking-3/
* Anatomy of DDoSia

Weekend #Plankton Factoid 🦠🦐
Many sessile marine organisms produce planktonic larvae for dispersal. This is true of the Bryozoa ("moss animals") in both freshwater and saltwater, which are often mistaken for sponges or corals. Larvae widely range in shape and lifestyle, but some larvae called cyphonautes can spend weeks feeding in the plankton. They are a distinctly flattened trian…

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
Carlos Garc\'ia A., Vladimiro Boselli, Aida Hejazi Nooghabi, Andrea Colombi, Lapo Boschi
https://arxiv.org/abs/2506.22944

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
This study introduces the first 3D spectral-element method (SEM) simulation of ultrasonic wave propagation in a bottlenose dolphin (Tursiops truncatus) head. Unlike traditional finite-element methods (FEM), which struggle with high-frequency simulations due to costly linear-system inversions and slower convergence, SEM offers exponential convergence and efficient parallel computation. Using Computed Tomography (CT) scan data, we developed a detailed hexahedral mesh capturing complex anatomical …

Anatomy of High-Performance Column-Pivoted QR Decomposition
Maksim Melnichenko, Riley Murray, William Killian, James Demmel, Michael W. Mahoney, Piotr Luszczek, Mark Gates
https://arxiv.org/abs/2507.00976

Anatomy of High-Performance Column-Pivoted QR Decomposition
We introduce an algorithmic framework for performing QR factorization with column pivoting (QRCP) on general matrices. The framework enables the design of practical QRCP algorithms through user-controlled choices for the core subroutines. We provide a comprehensive overview of how to navigate these choices on modern hardware platforms, offering detailed descriptions of alternative methods for both CPUs and GPUs. The practical QRCP algorithms developed within this framework are implemented as pa…

Robustness and Diagnostic Performance of Super-Resolution Fetal Brain MRI
Ema Masterl, Tina Vipotnik Vesnaver, \v{Z}iga \v{S}piclin
https://arxiv.org/abs/2509.10257 https://

Robustness and Diagnostic Performance of Super-Resolution Fetal Brain MRI
Fetal brain MRI relies on rapid multi-view 2D slice acquisitions to reduce motion artifacts caused by fetal movement. However, these stacks are typically low resolution, may suffer from motion corruption, and do not adequately capture 3D anatomy. Super-resolution reconstruction (SRR) methods aim to address these limitations by combining slice-to-volume registration and super-resolution techniques to generate high-resolution (HR) 3D volumes. While several SRR methods have been proposed, their co…

Anatomy of a QB failure, plus the rookie with an eerily similar profile to Jefferson https://www.nytimes.com/athletic/6565116/2025/08/20/colts-qb-situation-nfl-emeka-egbuka-fantasy-scoop-city/

Since at this point everyone surely has seen
The Big OOPs: Anatomy of a Thirty-five-year Mistake
(and quartz, of course)
At its 2.5h & focus on game dev, it’s a bit difficult to generally recommend, but it's of course catnip to ME.
And this slide everyone should get framed.
We are not biologists. We don’t need to build taxonomies on what something IS. Focus on DOING, not BEING.
Almost every OOP intro gets this wrong.

Anatomy of the gem5 Simulator: AtomicSimpleCPU, TimingSimpleCPU, O3CPU, and Their Interaction with the Ruby Memory System
Johan S\"oderstr\"om (Uppsala University), Yuan Yao (Uppsala University)
https://arxiv.org/abs/2508.18043

Anatomy of the gem5 Simulator: AtomicSimpleCPU, TimingSimpleCPU, O3CPU, and Their Interaction with the Ruby Memory System
gem5 is a popular modular-based computer system simulator, widely used in computer architecture research and known for its long simulation time and steep learning curve. This report examines its three major CPU models: the AtomicSimpleCPU (AS CPU), the TimingSimpleCPU (TS CPU), the Out-of-order (O3) CPU, and their interactions with the memory subsystem. We provide a detailed anatomical overview of each CPU's function call-chains and present how gem5 partitions its execution time for each simula…

It's #GrindayFriday! Brazil's LYMPHATIC PHLEGM are pathological goregrind legends, and their 2007 record 'Show-Off Cadavers - The Anatomy of Self-Display' is my favorite of theirs. Dynamics, melodies, killer riffs- you can hear how they've influenced Lipoma, Sulfuric Cautery, Tunkio, etc.
(2018 reprint):

Unlocking Mixed Reality for Medical Education: A See-Through Perspective on Head Anatomy
Yuqing Wei, Yupeng Wang, Jiayi Zhao, Yanjun Liu, Huxin Gao, Jiewen Lai
https://arxiv.org/abs/2509.00352

Unlocking Mixed Reality for Medical Education: A See-Through Perspective on Head Anatomy
Extended reality (XR), encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), is emerging as a transformative platform for medical education. Traditional methods such as textbooks, physical models, and cadaveric dissections often lack interactivity and fail to convey complex spatial relationships effectively. The emerging MR technology addresses these limitations by providing immersive environments that blend virtual elements with real-world contexts. This study pres…

The Anatomy of a Personal Health Agent
A. Ali Heydari, Ken Gu, Vidya Srinivas, Hong Yu, Zhihan Zhang, Yuwei Zhang, Akshay Paruchuri, Qian He, Hamid Palangi, Nova Hammerquist, Ahmed A. Metwally, Brent Winslow, Yubin Kim, Kumar Ayush, Yuzhe Yang, Girish Narayanswamy, Maxwell A. Xu, Jake Garrison, Amy Aremnto Lee, Jenny Vafeiadou, Ben Graef, Isaac R. Galatzer-Levy, Erik Schenck, Andrew Barakat, Javier Perez, Jacqueline Shreibati, John Hernandez, Anthony Z. Faranesh, Javier L. Prieto, Conn…

The Anatomy of a Personal Health Agent
Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. However, the application of health agents to fulfill the diverse needs of individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health agent that is able to reason about multimodal data from everyday consumer wellness devices and common personal health records, …

GRASPing Anatomy to Improve Pathology Segmentation
Keyi Li, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen
https://arxiv.org/abs/2508.03374 https://arx…

GRASPing Anatomy to Improve Pathology Segmentation
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudolabel integration and featur…

CardioComposer: Flexible and Compositional Anatomical Structure Generation with Disentangled Geometric Guidance
Karim Kadry, Shoaib Goraya, Ajay Manicka, Abdalla Abdelwahed, Farhad Nezami, Elazer Edelman
https://arxiv.org/abs/2509.08015

CardioComposer: Flexible and Compositional Anatomical Structure Generation with Disentangled Geometric Guidance
Generative models of 3D anatomy, when integrated with biophysical simulators, enable the study of structure-function relationships for clinical research and medical device design. However, current models face a trade-off between controllability and anatomical realism. We propose a programmable and compositional framework for guiding unconditional diffusion models of human anatomy using interpretable ellipsoidal primitives embedded in 3D space. Our method involves the selection of certain tissue…

Patient-Specific 3D Printed Dynamic Preoperative Planning Models in Modern Medicine
Keshav Jha, Joseph Mayer
https://arxiv.org/abs/2508.10134 https://arxiv…

Patient-Specific 3D Printed Dynamic Preoperative Planning Models in Modern Medicine
Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preope…

Computational Modeling for Personalized Transcranial Electrical Stimulation: Theory, Tools, and Applications
Mo Wang, Kexin Zheng, Yiling Liu, Huichun Luo, Tifei Yuan, Hongkai Wen, Pengfei Wei, Quanying Liu
https://arxiv.org/abs/2509.01192

Computational Modeling for Personalized Transcranial Electrical Stimulation: Theory, Tools, and Applications
Objective. Personalized transcranial electrical stimulation (tES) has gained growing attention due to the substantial inter-individual variability in brain anatomy and physiology. While previous reviews have discussed the physiological mechanisms and clinical applications of tES, there remains a critical gap in up-to-date syntheses focused on the computational modeling frameworks that enable individualized stimulation optimization. Approach. This review presents a comprehensive overview of rece…

FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes
Muraam Abdel-Ghani, Mahmoud Ali, Mohamed Ali, Fatmaelzahraa Ahmed, Mohamed Arsalan, Abdulaziz Al-Ali, Shidin Balakrishnan
https://arxiv.org/abs/2509.06159

FASL-Seg: Anatomy and Tool Segmentation of Surgical Scenes
The growing popularity of robotic minimally invasive surgeries has made deep learning-based surgical training a key area of research. A thorough understanding of the surgical scene components is crucial, which semantic segmentation models can help achieve. However, most existing work focuses on surgical tools and overlooks anatomical objects. Additionally, current state-of-the-art (SOTA) models struggle to balance capturing high-level contextual features and low-level edge features. We propose …

Pseudo-spectral model of elastic-wave propagation through toothed-whale head anatomy, and implications for biosonar
Fawad Ali, Carlos Garc\'ia A., Aida Hejazi-Nooghabi, Lapo Boschi
https://arxiv.org/abs/2508.15739

Pseudo-spectral model of elastic-wave propagation through toothed-whale head anatomy, and implications for biosonar
The sound-localization and, in particular, biosonar system of toothed whales is exceptionally performant. How this is achieved is not clear, given that: (i) toothed whales have no pinnae; (ii) while their auditory pathways have been studied in detail, no specific feature apparently replacing the pinna has been identified. In this study, we employ a pseudo-spectral time domain (PSTD) numerical scheme to model three-dimensional elastic wave propagation through a toothed-whale head including soft …

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Daniel Scholz, Ayhan Can Erdur, Robbie Holland, Viktoria Ehm, Jan C. Peeken, Benedikt Wiestler, Daniel Rueckert
https://arxiv.org/abs/2509.06592

Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditi…

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Maximilian Lueckel, Suhas Vijayakumar, Til Ole Bergmann
https://arxiv.org/abs/2506.22563

PlanTUS: A heuristic tool for prospective planning of transcranial ultrasound transducer placements
Low-intensity focused transcranial ultrasonic stimulation (TUS) offers unique depth and precision in non-invasive brain stimulation. Effective administration of TUS, however, requires precise placement of transducers to ensure selective neural target exposure and engagement. This process is constrained by individual skull anatomy, hardware limitations, as well as computational and time costs of running exhaustive acoustic simulations to optimize transducer placement. To address these challenges…

A Desktop-Centric Design Space for Direct Object Examination and Visualization in Mixed-Reality Environments
Sam Johnson-Lacoss, Santiago V. Lombeyda, S. George Djorgovski
https://arxiv.org/abs/2508.05088

A Desktop-Centric Design Space for Direct Object Examination and Visualization in Mixed-Reality Environments
Mixed reality (MR) environments are bound to become ubiquitous as MR technology becomes lighter, higher resolution, more affordable, and overall becomes a seamless extension of our current work and living spaces. For research scientists and clinicians focused on understanding 3D phenomena or patient pathologies within the context of the larger human anatomy, that means a necessary evolution of their workstations currently only utilizing 2D interfaces for everyday communication, logistics and da…

Crosslisted article(s) found for cs.MA. https://arxiv.org/list/cs.MA/new
[1/1]:
- The Anatomy of a Personal Health Agent
A. Ali Heydari, et al.
https://

AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Shuhan Ding, Jingjing Fu, Yu Gu, Naiteek Sangani, Mu Wei, Paul Vozila, Nan Liu, Jiang Bian, Hoifung Poon
https://arxiv.org/abs/2509.04819

AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations
Medical image synthesis has become an essential strategy for augmenting datasets and improving model generalization in data-scarce clinical settings. However, fine-grained and controllable synthesis remains difficult due to limited high-quality annotations and domain shifts across datasets. Existing methods, often designed for natural images or well-defined tumors, struggle to generalize to chest radiographs, where disease patterns are morphologically diverse and tightly intertwined with anatom…

'It's a toothache': Untold stories of NFL camp holdouts from players, agents, teams https://www.espn.com/nfl/story/_/id/45789518/nfl-training-camp-holdouts-trey-hendrickson-terry-mclaurin

X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis
Fabian Bongratz, Tom Nuno Wolf, Jaume Gual Ramon, Christian Wachinger
https://arxiv.org/abs/2506.20267

X-SiT: Inherently Interpretable Surface Vision Transformers for Dementia Diagnosis
Interpretable models are crucial for supporting clinical decision-making, driving advances in their development and application for medical images. However, the nature of 3D volumetric data makes it inherently challenging to visualize and interpret intricate and complex structures like the cerebral cortex. Cortical surface renderings, on the other hand, provide a more accessible and understandable 3D representation of brain anatomy, facilitating visualization and interactive exploration. Motiva…

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training
Weiwei Cao, Jianpeng Zhang, Zhongyi Shui, Sinuo Wang, Zeli Chen, Xi Li, Le Lu, Xianghua Ye, Tingbo Liang, Qi Zhang, Ling Zhang
https://arxiv.org/abs/2508.03742

Boosting Vision Semantic Density with Anatomy Normality Modeling for Medical Vision-language Pre-training
Vision-language pre-training (VLP) has great potential for developing multifunctional and general medical diagnostic capabilities. However, aligning medical images with a low signal-to-noise ratio (SNR) to reports with a high SNR presents a semantic density gap, leading to visual alignment bias. In this paper, we propose boosting vision semantic density to improve alignment effectiveness. On one hand, we enhance visual semantics through disease-level vision contrastive learning, which strengthe…

A complex network perspective on brain disease
David Papo, Javier M. Buld\'u
https://arxiv.org/abs/2507.23678 https://arxiv.org/pdf/2507.23678

A complex network perspective on brain disease
If brain anatomy and dynamics have a genuine complex network structure as it has become standard to posit, it is also reasonable to assume that such a structure should play a key role not only in brain function but also in brain dysfunction. However, exactly how network structure is implicated in brain damage and whether at least some pathologies can be thought of as "network diseases" is not entirely clear. Here we discuss ways in which a complex network representation can help characterising …

The Anatomy of Coronary Risk: How Artery Geometry Shapes Coronary Artery Disease through Blood Flow Haemodynamics -- Latest Methods, Insights and Clinical Implications
C. Shen, M. Zhang, H. Keramati, S. Zhang, R. Gharleghi, J. J. Wentzel, M. O. Khan, U. Morbiducci, A. Qayyum, S. A. Niederer, S. Samant, Y. S. Chatzizisis, D. Almeida, Tsung-Ying Tsai, P. Serruys, S. Beier

The Anatomy of Coronary Risk: How Artery Geometry Shapes Coronary Artery Disease through Blood Flow Haemodynamics -- Latest Methods, Insights and Clinical Implications
Despite tremendous advances in cardiovascular medicine, significant opportunities remain to improve coronary artery disease (CAD) prevention and treatment strategies. The key limitation lies in the understanding of disease formation and progression mechanisms. The coronary anatomy plays an important role in local haemodynamics, governing endothelial health and, thus, pathophysiological responses. The significant variation of the coronary anatomy among patients, with significant trends across di…

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Jisoo Kim, Chu-Hsuan Lin, Alberto Ceballos-Arroyo, Ping Liu, Huaizu Jiang, Shrikanth Yadav, Qi Wan, Lei Qin, Geoffrey S Young
https://arxiv.org/abs/2507.00832

Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Introduction: Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation, despite improvement in model architectures and strategies like detection threshold tuning. We employed an automated, anatomy-based, heuristic-learning hybrid artery-vein segmentation post-processing method to further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D convolutional neural network-transformer hybrid (3D-CN…

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Trixia Simangan, Ahmed Nadeem Abbasi, Yipeng Hu, Shaheer U. Saeed
https://arxiv.org/abs/2509.04886

Cryo-RL: automating prostate cancer cryoablation planning with reinforcement learning
Cryoablation is a minimally invasive localised treatment for prostate cancer that destroys malignant tissue during de-freezing, while sparing surrounding healthy structures. Its success depends on accurate preoperative planning of cryoprobe placements to fully cover the tumour and avoid critical anatomy. This planning is currently manual, expertise-dependent, and time-consuming, leading to variability in treatment quality and limited scalability. In this work, we introduce Cryo-RL, a reinforcem…

Investigation of a two-patch within-host model of hepatitis B viral infection
Keoni Castellano, Omar Saucedo, Stanca M. Ciupe
https://arxiv.org/abs/2507.23038 https://

Investigation of a two-patch within-host model of hepatitis B viral infection
Chronic infection with hepatitis B virus (HBV) can lead to formation of abnormal nodular structures within the liver. To address how changes in liver anatomy affect overall virus-host dynamics, we developed within-host ordinary differential equation models of two-patch hepatitis B infection, one that assumes irreversible and one that assumes reversible movement between nodular structures. We investigated the models analytically and numerically, and determined the contribution of patch susceptib…

ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging
Eichi Takaya, Ryusei Inamori
https://arxiv.org/abs/2508.18613 http…

ModAn-MulSupCon: Modality-and Anatomy-Aware Multi-Label Supervised Contrastive Pretraining for Medical Imaging
Background and objective: Expert annotations limit large-scale supervised pretraining in medical imaging, while ubiquitous metadata (modality, anatomical region) remain underused. We introduce ModAn-MulSupCon, a modality- and anatomy-aware multi-label supervised contrastive pretraining method that leverages such metadata to learn transferable representations. Method: Each image's modality and anatomy are encoded as a multi-hot vector. A ResNet-18 encoder is pretrained on a mini subset of RadI…

An Integrated Open Source Software System for the Generation and Analysis of Subject-Specific Blood Flow Simulation Ensembles
Simon Leistikow, Thomas Miro, Adrian Kummerl\"ander, Ali Nahardani, Katja Gr\"un, Markus Franz, Verena Hoerr, Mathias J. Krause, Lars Linsen
https://arxiv.org/abs/2509.09392

An Integrated Open Source Software System for the Generation and Analysis of Subject-Specific Blood Flow Simulation Ensembles
Background and Objective: Hemodynamic analysis of blood flow through arteries and veins is critical for diagnosing cardiovascular diseases, such as aneurysms and stenoses, and for investigating cardiovascular parameters, such as turbulence and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can be captured non-invasively using structural and 4D Magnetic Resonance Imaging (MRI). Computational Fluid Dynamics (CFD), on the other hand, can be used to gene…

Crosslisted article(s) found for q-bio.TO. https://arxiv.org/list/q-bio.TO/new
[1/1]:
- 3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
Mozyrska, Beetz, Melas-Kyriazi, Grau, Banerjee, Bueno-Orovio

🇺🇦 #NowPlaying on #BBC6Music's #GuyGarveysFinestHour
Terence Blanchard:
🎵 Anatomy of a Murder
#TerenceBlanchard

TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Yuxin Gong (for the Alzheimer's Disease Neuroimaging Initiative), Se-in Jang (for the Alzheimer's Disease Neuroimaging Initiative), Wei Shao (for the Alzheimer's Disease Neuroimaging Initiative), Yi Su (for the Alzheimer's Disease Neuroimaging Initiative), Kuang Gong (for the Alzheimer's Disease Neuroimaging Initiative)

TauGenNet: Plasma-Driven Tau PET Image Synthesis via Text-Guided 3D Diffusion Models
Accurate quantification of tau pathology via tau positron emission tomography (PET) scan is crucial for diagnosing and monitoring Alzheimer's disease (AD). However, the high cost and limited availability of tau PET restrict its widespread use. In contrast, structural magnetic resonance imaging (MRI) and plasma-based biomarkers provide non-invasive and widely available complementary information related to brain anatomy and disease progression. In this work, we propose a text-guided 3D diffusion …

Computational modeling of Pulsed Field Ablation for pulmonary vein isolation
Ashkan Bagherzadeh, Nagib T Chalfoun, Tong Gao, Lik Chuan Lee
https://arxiv.org/abs/2508.07133 https…

Computational modeling of Pulsed Field Ablation for pulmonary vein isolation
Pulsed field ablation (PFA) has emerged as a non-thermal alternative to traditional thermal ablation techniques for the treatment of atrial fibrillation (AF). This study presents a patient-specific 3D computational framework to model the effects of PFA on pulmonary vein isolation (PVI). The modeling framework is rigorously validated against published numerical and experimental data, demonstrating strong agreement across a range of scenarios. Using realistic left atrial (LA) anatomy, commerciall…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/9]:
- Full-Head Segmentation of MRI with Abnormal Brain Anatomy: Model and Data Release
Birnbaum, Buchwald, Turkeltaub, Jacks, Carra, Kannana, Huang, Datta, Parra, Hirsch

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Murong Xu, Tamaz Amiranashvili, Fernando Navarro, Maksym Fritsak, Ibrahim Ethem Hamamci, Suprosanna Shit, Bastian Wittmann, Sezgin Er, Sebastian M. Christ, Ezequiel de la Rosa, Julian Deseoe, Robert Graf, Hendrik M\"oller, Anjany Sekuboyina, Jan C. Peeken, Sven Becker, Giulia Baldini, Johannes Haubold, Felix Nensa, Ren\'e Hosch, Nikhil Mirajkar, Saad Khalid, Stefan Zachow, M…

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography
Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approaches typically target individual structures, creating a fragmented landscape of incompatible models with varying performance and disparate evaluation protocols. Foundational segmentation models address these limitations by providing a holistic anatomical view through a single model. Yet, robust clinical deployment deman…

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
Jiahao Huang, Ruifeng Li, Wenwen Yu, Anan Li, Xiangning Li, Mingchao Yan, Lei Xie, Qingrun Zeng, Xueyan Jia, Shuxin Wang, Ronghui Ju, Feng Chen, Qingming Luo, Hui Gong, Xiaoquan Yang, Yuanjing Feng, Zheng Wang
https://arxiv.org/abs/25…

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
The organization and connectivity of the arcuate fasciculus (AF) in nonhuman primates remain contentious, especially concerning how its anatomy diverges from that of humans. Here, we combined cross-scale single-neuron tracing - using viral-based genetic labeling and fluorescence micro-optical sectioning tomography in macaques (n = 4; age 3 - 11 years) - with whole-brain tractography from 11.7T diffusion MRI. Complemented by spectral embedding analysis of 7.0T MRI in humans, we performed a compa…

Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
Karin A. Olthof, Matteo Fusagli, Bianca G\"uttner, Tiziano Natali, Bram Westerink, Stefanie Speidel, Theo J. M. Ruers, Koert F. D. Kuhlmann, Andrey Zhylka
https://arxiv.org/abs/2508.14133

Automated surgical planning with nnU-Net: delineation of the anatomy in hepatobiliary phase MRI
Background: The aim of this study was to develop and evaluate a deep learning-based automated segmentation method for hepatic anatomy (i.e., parenchyma, tumors, portal vein, hepatic vein and biliary tree) from the hepatobiliary phase of gadoxetic acid-enhanced MRI. This method should ease the clinical workflow of preoperative planning. Methods: Manual segmentation was performed on hepatobiliary phase MRI scans from 90 consecutive patients who underwent liver surgery between January 2020 and O…

Cardiac mechanics modeling: recent developments and current challenges
Aaron L. Brown, Ju Liu, Daniel B. Ennis, Alison L. Marsden
https://arxiv.org/abs/2509.07971 https://

Cardiac mechanics modeling: recent developments and current challenges
Patient-specific computational models of the heart are powerful tools for cardiovascular research and medicine, with demonstrated applications in treatment planning, device evaluation, and surgical decision-making. Yet constructing such models is inherently difficult, reflecting the extraordinary complexity of the heart itself. Numerous considerations are required, including reconstructing the anatomy from medical images, representing myocardial mesostructure, capturing material behavior, defin…

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
Chun Xie, Yuichi Yoshii, Itaru Kitahara
https://arxiv.org/abs/2507.05148 https://

SV-DRR: High-Fidelity Novel View X-Ray Synthesis Using Diffusion Model
X-ray imaging is a rapid and cost-effective tool for visualizing internal human anatomy. While multi-view X-ray imaging provides complementary information that enhances diagnosis, intervention, and education, acquiring images from multiple angles increases radiation exposure and complicates clinical workflows. To address these challenges, we propose a novel view-conditioned diffusion model for synthesizing multi-view X-ray images from a single view. Unlike prior methods, which are limited in an…

Biological detail and graph structure in network neuroscience
David Papo, Javier M. Buld\'u
https://arxiv.org/abs/2507.15789 https://

Biological detail and graph structure in network neuroscience
Endowing brain anatomy, dynamics, and function with a network structure is becoming standard in neuroscience. In its simplest form, a network is a collection of units and relationships between them. The pattern of relations among the units encodes numerous properties which have been shown to have a profound effect on networked systems' dynamics and function. In an effort to strike a balance between idealization and detail, network neuroscience studies typically involve simplifying assumptions a…

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
Joris Wuts, Jakub Ceranka, Nicolas Michoux, Fr\'ed\'eric Lecouvet, Jef Vandemeulebroucke
https://arxiv.org/abs/2506.19590

Learning from Anatomy: Supervised Anatomical Pretraining (SAP) for Improved Metastatic Bone Disease Segmentation in Whole-Body MRI
The segmentation of metastatic bone disease (MBD) in whole-body MRI (WB-MRI) is a challenging problem. Due to varying appearances and anatomical locations of lesions, ambiguous boundaries, and severe class imbalance, obtaining reliable segmentations requires large, well-annotated datasets capturing lesion variability. Generating such datasets requires substantial time and expertise, and is prone to error. While self-supervised learning (SSL) can leverage large unlabeled datasets, learned generi…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/6]:
- Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition
Zefeng Qian, Xincheng Yao, Yifei Huang, Chongyang Zhang, Jiangyong Ying, Hong Sun

3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
Jolanta Mozyrska, Marcel Beetz, Luke Melas-Kyriazi, Vicente Grau, Abhirup Banerjee, Alfonso Bueno-Orovio
https://arxiv.org/abs/2508.14122

3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
Diffusion models have recently gained immense interest for their generative capabilities, specifically the high quality and diversity of the synthesized data. However, examples of their applications in 3D medical imaging are still scarce, especially in cardiology. Generating diverse realistic cardiac anatomies is crucial for applications such as in silico trials, electromechanical computer simulations, or data augmentations for machine learning models. In this work, we investigate the applicati…

REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Farzad Beizaee, Sina Hajimiri, Ismail Ben Ayed, Gregory Lodygensky, Christian Desrosiers, Jose Dolz
https://arxiv.org/abs/2508.02889

REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains challenging due to the intricate structure of brain anatomy and the scarcity of abnormal examples. In this work, we introduce REFLECT, a novel framework that leverages rectified flows to establish a direct, linear trajectory for correcting abnormal MR images toward a normal distribution. By learning a straight, one-step …

From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound
Max Kr\"ahenmann, Sergio Tascon-Morales, Fabian Laumer, Julia E. Vogt, Ece Ozkan
https://arxiv.org/abs/2508.14552

From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound
Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on specialized hardware and restrictive acquisition protocols. In this work, we present a novel unsupervised framework for reconstructing 3D anatomical structures from freehand 2D transvaginal ultrasound (TVS) sweeps, without requiring external tracking or learned pose estimators. Our method adapts the principles of Gaussian…

Mechanistic Insights Into How Rewiring and Bifurcation Angle Affect DK-Crush Stent Deployment
Andrea Colombo, Dario Carbonaro, Mingzi Zhang, Claudio Chiastra, Mark Webster, Nigel Jepson, Susann Beier
https://arxiv.org/abs/2509.02948

Mechanistic Insights Into How Rewiring and Bifurcation Angle Affect DK-Crush Stent Deployment
Background Double Kissing Crush (DKC) is a preferred two-stent technique for complex coronary bifurcation lesions. Proximal cell rewiring is routinely recommended to reduce technical failure, and DKC is considered effective across various bifurcation angles. However, it remains unclear whether this standard approach is optimal for all patients. This study investigates the interaction between bifurcation angle and rewiring configuration to identify anatomy-specific strategies. Methods Computatio…

Towards Digital Twins for Optimal Radioembolization
Nisanth Kumar Panneerselvam, Guneet Mummaneni, Emilie Roncali
https://arxiv.org/abs/2509.02607 https://…

Towards Digital Twins for Optimal Radioembolization
Radioembolization is a localized liver cancer treatment that delivers radioactive microspheres (30 micron) to tumors via a catheter inserted in the hepatic arterial tree. The goal is to maximize therapeutic efficacy while minimizing damage to healthy liver tissue. However, optimization is challenging due to complex hepatic artery anatomy, variable blood flow, and uncertainty in microsphere transport. The creation of dynamic, patient-specific digital twins may provide a transformative solution t…

Direct vascular territory segmentation on cerebral digital subtraction angiography
P. Matthijs van der Sluijs, Lotte Strong, Frank G. te Nijenhuis, Sandra Cornelissen, Pieter Jan van Doormaal, Geert Lycklama a Nijeholt, Wim van Zwam, Ad van Es, Diederik Dippel, Aad van der Lugt, Danny Ruijters, Ruisheng Su, Theo van Walsum
https://arxiv.or…

Direct vascular territory segmentation on cerebral digital subtraction angiography
X-ray digital subtraction angiography (DSA) is frequently used when evaluating minimally invasive medical interventions. DSA predominantly visualizes vessels, and soft tissue anatomy is less visible or invisible in DSA. Visualization of cerebral anatomy could aid physicians during treatment. This study aimed to develop and evaluate a deep learning model to predict vascular territories that are not explicitly visible in DSA imaging acquired during ischemic stroke treatment. We trained an nnUNet …

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Jian Wang, Qiongying Ni, Hongkui Yu, Ruixuan Yao, Jinqiao Ying, Bin Zhang, Xingyi Yang, Jin Peng, Jiongquan Chen, Junxuan Yu, Wenlong Shi, Chaoyu Chen, Zhongnuo Yan, Mingyuan Luo, Gaocheng Cai, Dong Ni, Jing Lu, Xin Yang
https://arxiv.org/abs/2507.00398

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from…

Crosslisted article(s) found for physics.med-ph. https://arxiv.org/list/physics.med-ph/new
[1/1]:
- Pseudo-spectral model of elastic-wave propagation through toothed-whale head anatomy, and implica...
Fawad Ali, Carlos Garc\'ia A., Aida Hejazi-Nooghabi, Lapo Boschi

Improving Prostate Gland Segmenting Using Transformer based Architectures
Shatha Abudalou
https://arxiv.org/abs/2506.14844 https://ar…

Improving Prostate Gland Segmenting Using Transformer based Architectures
Inter reader variability and cross site domain shift challenge the automatic segmentation of prostate anatomy using T2 weighted MRI images. This study investigates whether transformer models can retain precision amid such heterogeneity. We compare the performance of UNETR and SwinUNETR in prostate gland segmentation against our previous 3D UNet model [1], based on 546 MRI (T2weighted) volumes annotated by two independent experts. Three training strategies were analyzed: single cohort dataset, 5…

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
Lei Xu, Torkel B Brismar
https://arxiv.org/abs/2507.13901 …

Software architecture and manual for novel versatile CT image analysis toolbox -- AnatomyArchive
We have developed a novel CT image analysis package named AnatomyArchive, built on top of the recent full body segmentation model TotalSegmentator. It provides automatic target volume selection and deselection capabilities according to user-configured anatomies for volumetric upper- and lower-bounds. It has a knowledge graph-based and time efficient tool for anatomy segmentation mask management and medical image database maintenance. AnatomyArchive enables automatic body volume cropping, as wel…