Medical Image Segmentation Techniques

Últimos artículos

• OTLesMix: Wasserstein Barycenter and Optimal Transport Map for Synthetic Lesion Generation with Diverse Shapes and Locations

  Robin Trombetta, Carole Lartizien · 7 de agosto de 2026

  The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors from large volumes to characterize pathologies. Data augmentation is a technique widely regarded as a way to improve model training. It includes simpl…

• MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation

  John Garcia Henao, Nicholas B\"unger, Benedikt Herzog, Cindy Guerrero Toro, Benjamin Vella, Matthias Biner, Rico Br\"utsch, Carmen Castroviejo Fernandez, Felix \"Ottl, Norman Juchler, Armando Hoch, Bettina Hochreiter, Sven Hirsch, Sebastiano Caprara · 4 de agosto de 2026

  High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often fail under domain shift. CNN-based expert models are fully automatic but lack adaptability, whereas promptable foundation models generalize better but …

• Moment kernels: a simple and scalable approach for equivariance to rotations and reflections in deep convolutional networks

  Siqi Fang, Zachary Schlamowitz, Andrew Bennecke, Daniel J. Tward · 3 de agosto de 2026

  Translation equivariance is a central reason convolutional neural networks have been successful in computer vision. Other symmetries, such as rotations and reflections, are similarly important in fields such as biomedical image analysis, but equivariant methods for these symmetries remain less widel…

• U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation

  Elijah Danquah Darko, Min Xian, Terence Soule, Tiankai Yao, Matthew William Anderson · 24 de julio de 2026

  Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly. We propose Uncertainty-Guided Cascade Forward Refinement (U-CFR), a novel inference-time framework tha…

• When Does Consensus Beat Voting? A Critical Analysis of Statistical Label Fusion in Medical Image Segmentation

  Renjie He · 23 de julio de 2026

  This paper provides a rigorous, self-contained investigation of consensus segmentation. We derive the mathematical foundations from first principles -- the generative model, EM algorithm, Van Leemput's marginalization analysis, identifiability conditions, Spatial STAPLE, and deep variational formula…

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

  Francesco Fabbri, Martino Andrea Scarpolini, Paolo Ciancarella, Francesco Tudisco, Roberto Verzicco, Alessandro Ricci, Francesco Viola · 7 de julio de 2026

  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. However, most studies remain limited to single or patient-specific anatomies, restricting the inclusion of population…

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

  Jun Zeng, Quoc-Huy Trinh, Deepak Ranjan Nayak, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha · 29 de junio de 2026

  Liver cirrhosis plays a critical role in the prognosis of chronic liver disease. Early detection and timely intervention are essential for reducing mortality rates. However, the intricate anatomical architecture and diverse pathological changes of liver tissue complicate the accurate detection and c…

• Promise and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation: a feasibility study

  Jing Wang, Tong Yu, Hao-En Lu, Zixue Zeng, Joseph K. Leader, Xin Meng, Jianbing Zhu, Jiantao Pu · 24 de junio de 2026

  Purpose: To evaluate the feasibility and challenges of heart chamber segmentation from non-contrast CT scans using contrastive unpaired image translation and deep learning-based segmentation. Approach: We developed ChameleonNet, a framework utilizing the Contrastive Unpaired Translation (CUT) networ…

• EnTrust: Modeling Inter-Modal Conflict for Trustworthy Multimodal Medical Image Analysis

  Dwarikanath Mahapatra, Abhijit Das, Behzad Bozorgtabar, Zongyuan Ge, Sudipta Roy, Deepak Nayak, Mauricio Reyes, Imran Razzak · 23 de junio de 2026

  Multimodal medical imaging fuses complementary anatomical and functional information, yet modalities frequently disagree in pathologically heterogeneous regions. Current segmentation models handle this in one of two inadequate ways: deterministic fusion that averages away disagreement, or post-hoc u…

• Robustness of Similarity-based Positional Encoding Under Rotations: Theoretical Analysis and Experimental Validation

  Andrea Santomauro, Luigi Portinale, Giorgio Leonardi · 17 de junio de 2026

  Positional encoding is a fundamental component of Transformer architectures, as it injects information about the spatial or sequential arrangement of inputs. Among recent alternatives to standard absolute and sinusoidal encodings, similarity-based positional encoding (simPE) has emerged as a flexibl…

• Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation

  Li Liu, Mingzhu Wang, Zhenjiang Li, Da Chen, Laurent D. Cohen · 16 de junio de 2026

  Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance. Minimal path models have exhibited their strong ability in addressing image segmentation tasks. However, the per…

• From Sorting Algorithms to Scalable Kernels: Bayesian Optimization in High-Dimensional Permutation Spaces

  Zikai Xie, Linjiang Chen · 15 de junio de 2026

  Bayesian Optimization (BO) is a powerful tool for black-box optimization, but its application to high-dimensional permutation spaces is severely limited by the challenge of defining scalable representations. The current state-of-the-art BO approach for permutation spaces relies on an exhaustive $\Om…

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

  Daria Kern, Negar Chabi, Souraj Adhikary, Andre Mastmeyer · 9 de junio de 2026

  This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans. The method combines coarse-to-fine segmentation, predictions from multiple anatomical planes, and additional fuzzy 3D spatial maps that provide anatomical location cues …

• Geodesics with Unified Tangent-constrained Priors and Curvature Regularization

  Chong Di, Li Liu, Jinglin Zhang, Zhenjiang Li, Da Chen, Laurent D. Cohen · 2 de junio de 2026

  Curvature-penalized geodesic models have proven their effectiveness in image segmentation by computing globally optimal curves. Unfortunately, these models remain susceptible to shortcuts when delineating objects with complex shapes and image intensity distributions, as they lack mechanisms to enfor…

• Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures

  Prashanti Anderson, Mitali Bafna, Rares-Darius Buhai, Pravesh K. Kothari, David Steurer · 2 de junio de 2026

  We develop a new approach for clustering non-spherical (i.e., arbitrary component covariances) Gaussian mixture models via a subroutine, based on the sum-of-squares method, that finds a low-dimensional separation-preserving projection of the input data. Our method gives a non-spherical analog of the…

• KLIP: localized distribution shift detection via KL-divergence with diffusion priors in Inverse Problems

  Alireza Kheirandish, Jihoon Hong, Sara Fridovich-Keil · 1 de junio de 2026

  Diffusion models have shown promising performance as data-driven priors for computational imaging, as well as some capacity to detect out-of-distribution (OOD) images. However, existing approaches to OOD detection often require some knowledge of the shifted distribution, fail to detect subtle or loc…

• Adjusted Shuffling SARAH: Advancing Complexity Analysis via Dynamic Gradient Weighting

  Duc Toan Nguyen, Trang H. Tran, Lam M. Nguyen · 28 de mayo de 2026

  In this paper, we propose Adjusted Shuffling SARAH, a novel algorithm that integrates shuffling strategies into the recursive SARAH framework using a dynamic weighting mechanism to enhance exploration. We analyze the algorithm under two operating modes. First, we show that the Exact Mode matches the…

• Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation

  Farhana Yasmin, Mahade Hasan, Haipeng Liu, Amjad Ali, Ghulam Muhammad, Yu Xue · 12 de mayo de 2026

  Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remains difficult due to low tissue contrast, fuzzy boundaries, and inter scan variability. We present CardiacNAS, an evolutionary neural architecture searc…

• Scaling Vision Transformers for Functional MRI with Flat Maps

  Connor Lane, Mihir Tripathy, Leema Krishna Murali, Ratna Sagari Grandhi, Shamus Sim Zi Yang, Sam Gijsen, Debojyoti Das, Manish Ram, Utkarsh Kumar Singh, Cesar Kadir Torrico Villanueva, Yuxiang Wei, Will Beddow, Gianfranco Cort\'es, Suin Cho, Daniel Z. Kaplan, Benjamin Warner, Tanishq Mathew Abraham, Paul S. Scotti · 6 de mayo de 2026

  We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using the masked autoencoder framework on 2.1K hours of open fMRI data, and (2) we release the first open evaluation suite (Brain…

• Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images

  Rajalakshmi Palaniappan, Christoph Karg, Nemesio Navarro-Arambula, Peter Hirsch, Kristin Kraeker, Lisa Mais, Dagmar Kainmueller · 4 de mayo de 2026

  Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment-then-fix paradigm is fundamentally limited regarding its suitability for modeling the task of complete and topologically accurate vascular network …

• Multi-Stage Bi-Atrial Segmentation Framework from 3D Late Gadolinium-Enhanced MRI using V-Net Family Models

  Hao Wen, Jingsu Kang · 30 de abril de 2026

  We report our multi-stage framework designed for the problem of multi-class bi-atrial segmentation from 3D late gadolinium-enhanced (LGE) MRI of the human heart. The pipeline consists of a preprocessing step using multidimensional contrast limited adaptive histogram equalization (MCLAHE); coarse reg…

• MAE-Based Self-Supervised Pretraining for Data-Efficient Medical Image Segmentation Using nnFormer

  R. M. Krishna Sureddi, T. Satyanarayana Murthy, Nomula Varsha Reddy, Adi Kanishka, Nalla Manvika Reddy · 29 de abril de 2026

  Transformer architectures, including nnFormer,have demonstrated promising results in volumetric medical image segmentation by being able to capture long-range spatial interactions. Although they have high performance, these models need large quantities of labeled training data and are also likely to…

• Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

  Yifan Zhao, Egan Johnson, Prasanth Chatarasi, Vikram Adve, Sasa Misailovic · 21 de abril de 2026

  Operator fusion has become a key optimization for deep learning, which combines multiple deep learning operators to improve data reuse and reduce global memory transfers. However, existing tensor compilers struggle to fuse complex reduction computations involving loop-carried dependencies, such as a…

• Low-rank Orthogonalization for Large-scale Matrix Optimization with Applications to Foundation Model Training

  Chuan He, Zhanwang Deng, Zhaosong Lu · 21 de abril de 2026

  Neural network (NN) training is inherently a large-scale matrix optimization problem, yet the matrix structure of NN parameters has long been overlooked. Recently, the optimizer Muon \citep{jordanmuon}, which explicitly exploits this structure, has gained significant attention for its strong perform…

• Navigating Distribution Shifts in Medical Image Analysis: A Survey

  Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Frans Coenen, Amir Hussain, Kaizhu Huang · 21 de abril de 2026

  Medical Image Analysis (MedIA) has become indispensable in modern healthcare, enhancing clinical diagnostics and personalized treatment. Despite the remarkable advancements supported by deep learning (DL) technologies, their practical deployment faces challenges posed by distribution shifts, where m…