Tootfinder

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…

Real-Time, Population-Based Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Yiqun Lin, Haoran Sun, Yongqing Li, Rabia Aslam, Lung Fung Tse, Tiange Cheng, Chun Sing Chui, Wing Fung Yau, Victorine R. Le Meur, Meruyert Amangeldy, Kiho Cho, Yinyu Ye, James Zou, Wei Zhao, Xiaomeng Li
https://arxiv.org/abs/2508.13947

Real-Time, Population-Based Reconstruction of 3D Bone Models via Very-Low-Dose Protocols
Patient-specific bone models are essential for designing surgical guides and preoperative planning, as they enable the visualization of intricate anatomical structures. However, traditional CT-based approaches for creating bone models are limited to preoperative use due to the low flexibility and high radiation exposure of CT and time-consuming manual delineation. Here, we introduce Semi-Supervised Reconstruction with Knowledge Distillation (SSR-KD), a fast and accurate AI framework to reconstr…

Quest for a clinically relevant medical image segmentation metric: the definition and implementation of Medical Similarity Index
Szuzina Fazekas, Bettina Katalin Budai, Viktor B\'erczi, P\'al Maurovich-Horvat, Zsolt Vizi
https://arxiv.org/abs/2508.09722

Quest for a clinically relevant medical image segmentation metric: the definition and implementation of Medical Similarity Index
Background: In the field of radiology and radiotherapy, accurate delineation of tissues and organs plays a crucial role in both diagnostics and therapeutics. While the gold standard remains expert-driven manual segmentation, many automatic segmentation methods are emerging. The evaluation of these methods primarily relies on traditional metrics that only incorporate geometrical properties and fail to adapt to various applications. Aims: This study aims to develop and implement a clinically re…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Nadezhda Diadkina
https://arxiv.org/abs/2508.08351 https://…

An Interactive Platform for Unified Assessment of Drug-Drug Interactions Using Descriptive and Pharmacokinetic Data
Drug-drug interactions (DDIs) are a major concern in polypharmacy. Public databases often provide only qualitative descriptions without pharmacokinetic context. We present an interactive web tool that integrates 191,541 descriptive DDI records from DrugBank with 3,779 AUC-based interactions from the PK-DDIP dataset, extracted from FDA-approved drug labeling (DailyMed) using natural language processing and manual verification. Using multi-step name harmonization (exact, fuzzy, synonym expansion,…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Jinwei Zhang, Lianrui Zuo, Blake E. Dewey, Samuel W. Remedios, Yihao Liu, Savannah P. Hays, Dzung L. Pham, Ellen M. Mowry, Scott D. Newsome, Peter A. Calabresi, Aaron Carass, Jerry L. Prince
https://arxiv.org/a…

UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation
Automated segmentation of multiple sclerosis (MS) lesions using multicontrast magnetic resonance (MR) images improves efficiency and reproducibility compared to manual delineation, with deep learning (DL) methods achieving state-of-the-art performance. However, these DL-based methods have yet to simultaneously optimize in-domain accuracy and out-of-domain generalization when trained on a single source with limited data, or their performance has been unsatisfactory. To fill this gap, we propose …

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
Andreea Bianca Popescu, Andreas Seitz, Heiko Mahrholdt, Jens Wetzl, Athira Jacob, Lucian Mihai Itu, Constantin Suciu, Teodora Chitiboi
https://arxiv.org/abs/2507.00903

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection
Objectives Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual delineation. Traditional approaches relying on average relaxation values and single cutoffs may oversimplify myocardial complexity. This study evaluates whether deep learning (DL) can achieve segmentation accuracy comparable to inter-observer variability, explores the utility of statistical features beyond mean T1/T2 values, and assesses whether ma…