Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI
Fuente:
arXiv
Saved in:
| Main Authors: | Tran, Nicole, Prasad, Anisa, Zhuang, Yan, Mathai, Tejas Sudharshan, Kim, Boah, Lewis, Sydney, Mukherjee, Pritam, Liu, Jianfei, Summers, Ronald M. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
by: Zhuang, Yan, et al.
Published: (2024)
by: Zhuang, Yan, et al.
Published: (2024)
Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
by: Prasad, Anisa V., et al.
Published: (2025)
by: Prasad, Anisa V., et al.
Published: (2025)
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
by: Helm, Kimberly, et al.
Published: (2024)
by: Helm, Kimberly, et al.
Published: (2024)
Classification of Multi-Parametric Body MRI Series Using Deep Learning
by: Kim, Boah, et al.
Published: (2025)
by: Kim, Boah, et al.
Published: (2025)
Automated classification of multi-parametric body MRI series
by: Kim, Boah, et al.
Published: (2024)
by: Kim, Boah, et al.
Published: (2024)
Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation
by: Wang, Xinya, et al.
Published: (2025)
by: Wang, Xinya, et al.
Published: (2025)
Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors
by: Mathai, Tejas Sudharshan, et al.
Published: (2024)
by: Mathai, Tejas Sudharshan, et al.
Published: (2024)
Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
by: Hou, Benjamin, et al.
Published: (2025)
by: Hou, Benjamin, et al.
Published: (2025)
Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study
by: Hou, Benjamin, et al.
Published: (2024)
by: Hou, Benjamin, et al.
Published: (2024)
Longitudinal Assessment of Lung Lesion Burden in CT
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
Correcting Class Imbalances with Self-Training for Improved Universal Lesion Detection and Tagging
by: Shieh, Alexander, et al.
Published: (2025)
by: Shieh, Alexander, et al.
Published: (2025)
3D Universal Lesion Detection and Tagging in CT with Self-Training
by: Frazier, Jared, et al.
Published: (2025)
by: Frazier, Jared, et al.
Published: (2025)
Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT
by: Erickson, Peter D., et al.
Published: (2025)
by: Erickson, Peter D., et al.
Published: (2025)
Utility of Pancreas Surface Lobularity as a CT Biomarker for Opportunistic Screening of Type 2 Diabetes
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
Text Embedded Swin-UMamba for DeepLesion Segmentation
by: Cheng, Ruida, et al.
Published: (2025)
by: Cheng, Ruida, et al.
Published: (2025)
A Study of Anatomical Priors for Deep Learning-Based Segmentation of Pheochromocytoma in Abdominal CT
by: Toma, Tanjin Taher, et al.
Published: (2025)
by: Toma, Tanjin Taher, et al.
Published: (2025)
Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
by: Mathai, Tejas Sudharshan, et al.
Published: (2025)
Weakly-Supervised Detection of Bone Lesions in CT
by: Sheng, Tao, et al.
Published: (2024)
by: Sheng, Tao, et al.
Published: (2024)
Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study
by: Nguyen, Andrew M., et al.
Published: (2024)
by: Nguyen, Andrew M., et al.
Published: (2024)
Weakly Supervised Detection of Pheochromocytomas and Paragangliomas in CT
by: Oluigboa, David C., et al.
Published: (2024)
by: Oluigboa, David C., et al.
Published: (2024)
Learning to Segment Corneal Tissue Interfaces in OCT Images
by: Mathai, Tejas Sudharshan, et al.
Published: (2018)
by: Mathai, Tejas Sudharshan, et al.
Published: (2018)
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
by: Zhu, Qingqing, et al.
Published: (2024)
by: Zhu, Qingqing, et al.
Published: (2024)
Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation
by: Krishnaswamy, Deepa, et al.
Published: (2025)
by: Krishnaswamy, Deepa, et al.
Published: (2025)
How Well Do Multi-modal LLMs Interpret CT Scans? An Auto-Evaluation Framework for Analyses
by: Zhu, Qingqing, et al.
Published: (2024)
by: Zhu, Qingqing, et al.
Published: (2024)
Self and Mixed Supervision to Improve Training Labels for Multi-Class Medical Image Segmentation
by: Liu, Jianfei, et al.
Published: (2024)
by: Liu, Jianfei, et al.
Published: (2024)
Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification
by: Lanfredi, Ricardo Bigolin, et al.
Published: (2024)
by: Lanfredi, Ricardo Bigolin, et al.
Published: (2024)
A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation
by: Huang, Ziyan, et al.
Published: (2023)
by: Huang, Ziyan, et al.
Published: (2023)
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation
by: Jha, Debesh, et al.
Published: (2024)
by: Jha, Debesh, et al.
Published: (2024)
LEAVS: An LLM-based Labeler for Abdominal CT Supervision
by: Lanfredi, Ricardo Bigolin, et al.
Published: (2025)
by: Lanfredi, Ricardo Bigolin, et al.
Published: (2025)
Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset
by: Bayer, Lukas, et al.
Published: (2026)
by: Bayer, Lukas, et al.
Published: (2026)
Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification
by: Hou, Benjamin, et al.
Published: (2024)
by: Hou, Benjamin, et al.
Published: (2024)
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
by: Belton, Niamh, et al.
Published: (2026)
by: Belton, Niamh, et al.
Published: (2026)
Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels
by: Ciausu, Cosmin, et al.
Published: (2024)
by: Ciausu, Cosmin, et al.
Published: (2024)
Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation
by: Jingyu Zhong, et al.
Published: (2026)
by: Jingyu Zhong, et al.
Published: (2026)
Editorial for “Assessment of Robustness of MRI Radiomic Features in Four Abdominal Organs: Impact of Deep Learning Reconstruction and Segmentation”
by: Grace McIlvain, et al.
Published: (2026)
by: Grace McIlvain, et al.
Published: (2026)
FMD-TransUNet: Abdominal Multi-Organ Segmentation Based on Frequency Domain Multi-Axis Representation Learning and Dual Attention Mechanisms
by: Lu, Fang, et al.
Published: (2025)
by: Lu, Fang, et al.
Published: (2025)
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification
by: Hou, Benjamin, et al.
Published: (2024)
by: Hou, Benjamin, et al.
Published: (2024)
Quantifying the Impact of Population Shift Across Age and Sex for Abdominal Organ Segmentation
by: Čevora, Kate, et al.
Published: (2024)
by: Čevora, Kate, et al.
Published: (2024)
CT-Bench: A Benchmark for Multimodal Lesion Understanding in Computed Tomography
by: Zhu, Qingqing, et al.
Published: (2026)
by: Zhu, Qingqing, et al.
Published: (2026)
McCaD: Multi-Contrast MRI Conditioned, Adaptive Adversarial Diffusion Model for High-Fidelity MRI Synthesis
by: Dayarathna, Sanuwani, et al.
Published: (2024)
by: Dayarathna, Sanuwani, et al.
Published: (2024)
Similar Items
-
MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI
by: Zhuang, Yan, et al.
Published: (2024) -
Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT
by: Prasad, Anisa V., et al.
Published: (2025) -
Automated Classification of Body MRI Sequence Type Using Convolutional Neural Networks
by: Helm, Kimberly, et al.
Published: (2024) -
Classification of Multi-Parametric Body MRI Series Using Deep Learning
by: Kim, Boah, et al.
Published: (2025) -
Automated classification of multi-parametric body MRI series
by: Kim, Boah, et al.
Published: (2024)