arXiv AI

Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps

arXiv:2606. 07717v1 Announce Type: cross Abstract: This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans.

arXiv Machine Learning

Jul 27

Bowel Obstruction Detection and Localization on Abdominal CT with Deep Learning

arXiv:2607. 22173v1 Announce Type: cross Abstract: Bowel obstruction is a common and potentially life-threatening gastrointestinal condition.

By Moritz Vandenhirtz, Andrea Agostini, Dana Belde, M\'elanie Roschewitz, Ismaiel Chikh Bakri, Tilo Niemann, Andr\'e Euler, Julia E Vogt

arXiv AI

Jun 29

SRMA-Mamba: Spatial Reverse Mamba Attention Network for Pathological Liver Segmentation in MRI Volumes

arXiv:2508. 12410v3 Announce Type: replace-cross Abstract: Liver cirrhosis plays a critical role in the prognosis of chronic liver disease.

By Jun Zeng, Quoc-Huy Trinh, Deepak Ranjan Nayak, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha

computer-vision benchmarks

arXiv AI

Jun 17

High-Fidelity 3D Geometric Reconstruction of Pelvic Organs from MRI: A Hybrid Deep Learning and Iterative Optimization Approach

arXiv:2606. 17836v1 Announce Type: cross Abstract: Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis.

By Hui Wang, Xiaowei Li, Chenxin Zhang, Yifan Feng, Jianwei Zuo, Yumeng Tang, Xiuli Sun, Jianliu Wang, Bing Xie, Jiajia Luo

computer-vision

arXiv AI

2d ago

INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT

arXiv:2512. 14732v3 Announce Type: replace-cross Abstract: Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines.

By Idan Tankel, Nir Mazor, Rafi Brada, Christina LeBedis, Guy ben-Yosef

llms agents computer-vision multimodal benchmarks

arXiv Machine Learning

Jun 16

CT-VDETR: Semi-supervised 3D Trauma Detection in Computed Tomography (CT) scans using Dense Vertex Relative Position Encoding

arXiv:2603. 12514v2 Announce Type: replace-cross Abstract: Accurate detection and localization of traumatic injuries in abdominal CT remain challenging because voxel-level annotations are limited and expensive to obtain.

By Shivam Chaudhary, Sheethal Bhat, Andreas Maier

llms benchmarks

arXiv AI

Jul 7

From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

arXiv:2607. 02564v1 Announce Type: cross Abstract: Computational models of the human heart are widely used to study electromechanical and fluid-dynamical cardiac function and to support applications such as in silico clinical trials.

By Francesco Fabbri, Martino Andrea Scarpolini, Paolo Ciancarella, Francesco Tudisco, Roberto Verzicco, Alessandro Ricci, Francesco Viola

diffusion computer-vision

arXiv AI

Jul 16

Multimodal Assessment of Pancreatic Cancer Resectability Using Deep Learning

arXiv:2607. 13826v1 Announce Type: cross Abstract: Accurate determination of pancreatic ductal adenocarcinoma (PDAC) resectability relies on evaluating how the tumor interacts with major peripancreatic vessels on CT imaging, yet expert assessment often shows substantial variability.

By Vincent Ochs, Christoph Kuemmerli, Florentin Bieder, Julia Wolleb, Joel L. Lavanchy, Julia Ruppel, Jan Liechti, Stephanie Taha-Mehlitz, Christian Andreas Nebiker, Beat Mueller, Giuseppe Kito Fusai, Joerg-Matthias Pollok, Anas Taha, Philippe C. Cattin, Sebastian Staubli

rag computer-vision multimodal

arXiv AI

23h ago

Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction

arXiv:2608. 17255v1 Announce Type: cross Abstract: X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording the accumulated attenuation along a corresponding ray path.

By Yifei Wu, Yicheng Wu, Qiang Ma, Qi Chen, Renyang Gu, Xinyu Liu, Yongsheng Pan, Yong Xia

computer-vision

arXiv Machine Learning

Jun 16

MNet++: Extended 2D/3D Networks for Anisotropic Medical Image Segmentation

arXiv:2606. 15370v1 Announce Type: cross Abstract: This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation.

By Kirsten Odendaal, Rade Bajic

computer-vision

arXiv AI

Jun 9

Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework

arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.

By Sarah de Boer, Hartmut H\"antze, Kiran Vaidhya Venkadesh, Myrthe A. D. Buser, Gabriel E. Humpire Mamani, Lina Xu, Lisa C. Adams, Jawed Nawabi, Keno K. Bressem, Bram van Ginneken, Mathias Prokop, Alessa Hering

computer-vision benchmarks

arXiv AI

Jun 16

A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond

arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.

By Pengyu Zhu, Xiaojing Zhang, Kunbo Zhang, Chunyan Zhang, Zhenyu Wang

llms computer-vision benchmarks

arXiv AI

Jun 24

Render-FM: Feedforward Model for Real-time Photorealistic Volumetric Rendering

arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.

By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu

computer-vision fine-tuning

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