Medical Image Segmentation Techniques

Últimos artículos

• How Far Can INRs Go? Cross-Domain Parameter-efficient INR-Based Semantic Segmentation for Brain MRI

  Ziyao Shang, Pouya Sadeghi, Letian Jiang, Alexander Wong, Sirisha Rambhatla · 28 de septiembre de 2026

  Biomedical image segmentation is central to medical image analysis, but practical deployment often faces limited annotations, memory constraints, and cross-site distribution shifts. Implicit Neural Representations (INRs) have recently emerged as a lightweight alternative for semantic segmentation, a…

• SAMI3D-DW: Interactive Segmentation of Any 3D Medical Images

  Ping Gong, Shiyuan Su, Fandong Zhang, Xinchen Han, Haowei Sun, Yiming Li, Yizhou Yu · 25 de septiembre de 2026

  Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and refine their targets. Despite substantial progress by nnInteractive and VISTA3D, reliable segmentation across diverse clinical targets remains challen…

• BiCC: Bidirectional Connected-Component Loss for Instance-Aware Segmentation

  Luc Bouteille, Frederic Jonske, Jens Kleesiek, Alexander Jaus · 25 de septiembre de 2026

  Common segmentation losses aggregate errors voxel-wise, so lesions influence the objective in proportion to their volume, giving small but clinically critical lesions disproportionately little weight. Instance-aware losses aim to address this mismatch by assigning each lesion its own term. However, …

• Mind the Gap: Mesh-Guided Repair of Broken Vessels

  Gniewosz Drwiega, Wojciech Szymanski, Marek Wodzinski · 25 de septiembre de 2026

  Vessel segmentation is commonly optimized as voxel-wise classification, but small local errors can strongly disrupt vascular connectivity while having little effect on overlap scores. This is particularly problematic for downstream analyses that rely on centerlines, branches, connected components, o…

• Recursive Uncertainty-Gated Image Registration for Learning-based Algorithms

  Clara Rodrigo Gonz\'alez, Oscar Bates, Fu Siong Ng, Meng-Xing Tang · 24 de septiembre de 2026

  Conventional image registration algorithms are robust to domain shifts and achieve low errors, but they are slow and computationally expensive. Deep-learning methods are efficient at inference-time, but face challenges in out-of-domain samples. We propose Recursive Uncertainty-Gated Image Registrati…

• Learning Spectral Allocation: A Fractional Diffusion Framework for Adaptive Volumetric Segmentation

  Yi-Hui Shen, Tie-Qiang Li · 24 de septiembre de 2026

  We address adaptive computation in 3D medical image segmentation: instead of designing another backbone, we ask how much spectral mixing each network stage needs and let optimization answer. We derive FHEAT, a two-parameter operator family, from the discrete cosine transform (DCT) solution of a frac…

• Single Point, Full Mask: Velocity-Guided Level Set Evolution for End-to-End Amodal Segmentation

  Zhixuan Li, Yujia Liu, Chen Hui, Chenyue Song, Weisi Lin · 22 de septiembre de 2026

  Amodal segmentation aims to recover complete object shapes, including occluded regions, serving as an essential technique for user-centric multimedia authoring and object-level visual manipulation. Existing methods typically rely on informative prompts, such as bounding boxes or dense visible masks,…

• Confidence-Aware Teacher-Student Distillation for 3D Medical Segmentation

  Georgios Triantafyllou, Dimitris K. Iakovidis · 22 de septiembre de 2026

  Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and imaging modalities. This work addresses the challenge of predicting entire 3D anatomical structures from extreme annotation sparsity. An annotation-effi…

• Yarn tracking of large-scale 3D textile reinforcements using topological material features

  Hafsa El Herichi (LMPS, CVN), Arturo Mendoza (LMPS), Yanneck Wielhorski (CVN), Hugues Talbot (CVN), St\'ephane Roux (LMPS) · 22 de septiembre de 2026

  Automated segmentation of CT images has become increasingly important to enhance the reliability of simulations through the generation of high fidelity numerical models. This study addresses the challenging task of semi-automatically tracking textile reinforcements in fan blade dry preforms using X-…

• UniReg: Conditional Unified Model for Medical Image Registration

  Zi Li, Jianpeng Zhang, Tai Ma, Tony C. W. Mok, Yan-Jie Zhou, Zeli Chen, Xianghua Ye, Le Lu, Cheng Chen, Dakai Jin · 18 de septiembre de 2026

  Learning-based medical image registration has matched the accuracy of conventional methods while offering superior computational efficiency. However, existing approaches suffer from poor generalization across diverse clinical scenarios, requiring the laborious development of multiple isolated networ…

• G^2RA-NET: Graph-based Cross-Slice Relation Modeling with Attention Gating for Medical Image Segmentation

  Shengye Wang, Zonglin Wu, Liang Fan, Yule Xue, Haozhe Zhao · 18 de septiembre de 2026

  Medical image segmentation supports quantitative clinical analysis and computer-aided diagnosis. Recent methods for medical image segmentation have improved both local feature representation and volumetric context modeling. However, existing methods still strug- gle to efficiently model cross-slice …

• BINDER: A Latent Variable Model for Probabilistic Medical Image Registration

  Stefano Cerri, Amirhossein Hassankhani, Ya\"el Balbastre, Koen Van Leemput · 18 de septiembre de 2026

  We propose a new probabilistic model for general-purpose medical image registration that builds upon the mutual information registration criterion. It centers around a spatial interpolation technique that assumes latent voxel-wise correspondences between the images being registered. By exploiting th…

• Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation

  Dakini Mallam Garba, Salim Abdou Daoura · 18 de septiembre de 2026

  Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limitations, we propose a two-stage learning framework that efficiently leverages partial supervision. In the first stage, the model learns from available…

• Differentiable Mesh State Estimation via Factor Graph Inference for Deformable Object Reconstruction

  Lidia Al-Zogbi, Fangjie Li, Samuel Tobin, James Ferguson, Nithesh Kumar, Alejandro Chara, Kuan-I Chung, Mingxing Rao, Ayberk Acar, Susheela Sharma Stern, Robert Webster, Daniel Moyer, Alan Kuntz, Caleb Rucker, Tucker Hermans, Jie Ying Wu · 16 de septiembre de 2026

  Estimating deformable object states remains a fundamental challenge in robotics and simulation. We propose a novel factor graph-based framework for probabilistic mesh state estimation of deformable objects. The method directly updates a tetrahedral mesh, a rich and physically-grounded representation…

• IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets

  Abhilaksh Singh Reen, Kushal Borkar, Ritvik Mahapatra · 16 de septiembre de 2026

  Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods reduce this effort but stay interaction-inefficient: slice-wise methods (including many foundation models) ignore inter-slice continuity, while 3D and …

• Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration

  Matteo Barbieri, Giammarco La Barbera, Juan Pablo De La Plata, Sabine Sarnacki, Isabelle Bloch, Pietro Gori · 16 de septiembre de 2026

  Multimodal image registration is a key component of many clinical workflows, yet it remains challenging because corresponding anatomical structures often exhibit substantially different image intensities across modalities. In this work, we present a comprehensive benchmark of intra-patient 3D multim…

• SegKAN: High-Resolution Medical Image Segmentation with Long-Distance Dependencies

  Shengbo Tan, Rundong Xue, Shipeng Luo, Zeyu Zhang, Xinran Wang, Lei Zhang, Daji Ergu, Zhang Yi, Yang Zhao, Ying Cai · 11 de septiembre de 2026

  Hepatic vessels in computed tomography scans often suffer from image fragmentation and noise interference, making it difficult to maintain vessel integrity and posing significant challenges for vessel segmentation. To address this issue, we propose an innovative model: SegKAN. First, we improve the …

• Fast and Accurate Monomodal 3D High Resolution Deep Registration of Drosophila Larval Brain Volumes

  Daniel Reisenb\"uchler, Yousef Sadegheih, Michael Dittrich, Pratibha Kumari, Muhammad Usman, Dorit Merhof · 11 de septiembre de 2026

  The larval stage of Drosophila melanogaster is a compact model system for neuroscience whose genetic toolkit allows fluorescent markers to be expressed in defined neural populations, and comparing the resulting expression patterns across animals requires every brain to be registered into a shared an…

• Two-Parameter Flow Map Learning for Continuous-Time Diffeomorphic Image Registration

  Mohammadjavad Matinkia, Nilanjan Ray · 11 de septiembre de 2026

  Diffeomorphic image registration is central to medical image analysis, enabling anatomically consistent alignment across subjects. Most learning-based diffeomorphic methods model autonomous ODEs(ordinary differential equations) by parameterizing a stationary velocity field and recovering deformation…

• When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation

  Yuchen Pei, Xiaoyu Hu, Yixiong Zou, Dingwen Hu, Hui Chu, Yutao Ma, Shijun Qiu, Gang Li · 10 de septiembre de 2026

  Multi-modal medical image segmentation leverages complementary diagnostic information, yet fusion can underperform single-modality baselines when spatially aligned inputs differ in quality. Here, "corruption" primarily denotes resolution-induced degradation rather than misalignment or complete modal…

• Latent-to-Latent Flow for Volumetric Stochastic Segmentation

  Omar Todd, Sooha Kim, Raghav Mehta, Katherine Mackay, David Bernstein, Alexandra Taylor, Fabio De Sousa Ribeiro, Ben Glocker · 9 de septiembre de 2026

  Uncertainty arising from inter-observer variability in medical image segmentation plays an important role in developing treatment plans. Research in this area is inhibited by the lack of multiple annotations for large-scale medical datasets, especially for volumetric data, which suffers from additio…

• Weakly supervised neural network: segmentation of complex structures in X-ray microCT

  Daniele Rusconi, Michela Ascolese, Stephanie Fest-Santini, Alberto Bravin, Maurizio Santini · 9 de septiembre de 2026

  Segmentation of complex structures in X-ray tomographic data is a fundamental task in biomedical research, but it often requires large amounts of precisely annotated data, making fully supervised approaches costly and difficult to scale. In this study, weakly supervised deep learning is investigated…

• Auditing Patient Privacy in Medical Generative Models: Scalable Memorization Detection with DeepSSIM++

  Antonio Scardace, Francesco Guarnera, Sebastiano Battiato, Daniele Rav\`i · 4 de septiembre de 2026

  While deep generative models offer new opportunities for medical image synthesis and data sharing, their ability to memorize and reproduce training samples raises serious concerns about patient confidentiality. Detecting such memorization at scale remains challenging: traditional pixel-based metrics…

• SAUF-Net: Structure--Appearance Representation Learning with Uncertainty Feedback for Semi-Supervised Medical Image Segmentation

  Qin Lu, Zheyang Jing, Yujie Yang, Jianwang Li, Chen Yi, Shaofeng Jiang · 3 de septiembre de 2026

  Semi-supervised learning has shown great potential for reducing annotation costs in medical image segmentation. However, most existing methods mainly exploit unlabeled data through prediction-level consistency, while the reliability of internal feature representations is often overlooked. In medical…

• Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

  Luca Ciampi, Gabriele Lagani, Giuseppe Amato, Fabrizio Falchi · 2 de septiembre de 2026

  Supervised deep learning achieves strong performance in biomedical image segmentation but relies on costly pixel-wise annotations, motivating semi-supervised approaches that exploit unlabeled data. We introduce a diffusion-based teacher--student framework in which segmentation predictions are used t…