Medical Image Segmentation Techniques

Derniers papiers

• Efficient Search of Implantable Adaptive Cells for Medical Image Segmentation

  Emil Benedykciuk, Marcin Denkowski, Grzegorz M. W\'ojcik · 17 avril 2026

  Purpose: Adaptive skip modules can improve medical image segmentation, but searching for them is computationally costly. Implantable Adaptive Cells (IACs) are compact NAS modules inserted into U-Net skip connections, reducing the search space compared with full-network NAS. However, the original IAC…

• Energy-based Tissue Manifolds for Longitudinal Multiparametric MRI Analysis

  Kartikay Tehlan, Lukas F\"orner, Nico Schmutzenhofer, Michael Fr\"uhwald, Matthias Wagner, Nassir Navab, Thomas Wendler · 10 avril 2026

  We propose a geometric framework for longitudinal multi-parametric MRI analysis based on patient-specific energy modelling in sequence space. Rather than operating on images with spatial networks, each voxel is represented by its multi-sequence intensity vector ($T1$, $T1c$, $T2$, FLAIR, ADC), and a…

• Controllable Image Generation with Composed Parallel Token Prediction

  Jamie Stirling, Noura Al-Moubayed, Chris G. Willcocks, Hubert P. H. Shum · 8 avril 2026

  Conditional discrete generative models struggle to faithfully compose multiple input conditions. To address this, we derive a theoretically-grounded formulation for composing discrete probabilistic generative processes, with masked generation (absorbing diffusion) as a special case. Our formulation …

• Aleatoric Uncertainty Medical Image Segmentation Estimation via Flow Matching

  Phi Van Nguyen, Ngoc Huynh Trinh, Duy Minh Lam Nguyen, Phu Loc Nguyen, Quoc Long Tran · 8 avril 2026

  Quantifying aleatoric uncertainty in medical image segmentation is critical since it is a reflection of the natural variability observed among expert annotators. A conventional approach is to model the segmentation distribution using the generative model, but current methods limit the expression abi…

• Scale-adaptive and robust intrinsic dimension estimation via optimal neighbourhood identification

  Antonio Di Noia, Iuri Macocco, Aldo Glielmo, Alessandro Laio, Antonietta Mira · 2 avril 2026

  The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in almost any real-world dataset the ID depends on the scale at which the data are analysed. Quite typica…

• NeST-BO: Fast Local Bayesian Optimization via Newton-Step Targeting of Gradient and Hessian Information

  Wei-Ting Tang, Akshay Kudva, Joel A. Paulson · 30 mars 2026

  Bayesian optimization (BO) is effective for expensive black-box problems but remains challenging in high dimensions. We propose NeST-BO, a curvature-aware local BO method that targets a (modified) Newton step by jointly learning gradient and Hessian information with Gaussian process (GP) surrogates,…

• From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation

  Tao Wang, Zhenxuan Zhang, Yuanbo Zhou, Xinlin Zhang, Yuanbin Chen, Tao Tan, Guang Yang, Tong Tong · 25 mars 2026

  The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Even expert-labeled datasets inevitably contain noise arising from subjectivity and coarse delineations, which disrupt feat…

• Cycle Inverse-Consistent TransMorph: A Balanced Deep Learning Framework for Brain MRI Registration

  Jiaqi Shang, Haojin Wu, Yinyi Lai, Zongyu Li, Chenghao Zhang, Jia Guo · 24 mars 2026

  Deformable image registration plays a fundamental role in medical image analysis by enabling spatial alignment of anatomical structures across subjects. While recent deep learning-based approaches have significantly improved computational efficiency, many existing methods remain limited in capturing…

• ARIADNE: A Perception-Reasoning Synergy Framework for Trustworthy Coronary Angiography Analysis

  Zhan Jin, Yu Luo, Yizhou Zhang, Ziyang Cui, Yuqing Wei, Xianchao Liu, Xueying Zeng, Qing Zhang · 20 mars 2026

  Conventional pixel-wise loss functions fail to enforce topological constraints in coronary vessel segmentation, producing fragmented vascular trees despite high pixel-level accuracy. We present ARIADNE, a two-stage framework coupling preference-aligned perception with RL-based diagnostic reasoning f…

• LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging

  Athira J. Jacob, Puneet Sharma, Daniel Rueckert · 20 mars 2026

  Segmentation of enhancement in LGE cardiac MRI is critical for diagnosing various ischemic and non-ischemic cardiomyopathies. However, creating pixel-level annotations for these images is challenging and labor-intensive, leading to limited availability of annotated data. Generative models, particula…

• Structured SIR: Efficient and Expressive Importance-Weighted Inference for High-Dimensional Image Registration

  Ivor J. A. Simpson, Neill D. F. Campbell · 19 mars 2026

  Image registration is an ill-posed dense vision task, where multiple solutions achieve similar loss values, motivating probabilistic inference. Variational inference has previously been employed to capture these distributions, however restrictive assumptions about the posterior form can lead to poor…

• Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation

  Domen Prelo\v{z}nik, \v{Z}iga \v{S}piclin · 17 mars 2026

  Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that effic…

• RefTr: Recurrent Refinement of Confluent Trajectories for 3D Vascular Tree Centerlines

  Roman Naeem, David Hagerman, Jennifer Alv\'en, Fredrik Kahl · 13 mars 2026

  Tubular tree structures such as blood vessels and lung airways are central to many clinical tasks, including diagnosis, treatment planning, and surgical navigation. Accurate centerline extraction with correct topology is essential, as missing small branches can lead to incomplete assessments or over…

• FusionNet: a frame interpolation network for 4D heart models

  Chujie Chang, Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume, Oscar Martinez Mozos · 12 mars 2026

  Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40-60 min, which increases patient discomfort. In addition, shorter scan times de…

• Divide and Predict: An Architecture for Input Space Partitioning and Enhanced Accuracy

  Fenix W. Huang, Henning S. Mortveit, Christian M. Reidys · 10 mars 2026

  In this article the authors develop an intrinsic measure for quantifying heterogeneity in training data for supervised learning. This measure is the variance of a random variable which factors through the influences of pairs of training points. The variance is shown to capture data heterogeneity and…

• Self-Auditing Parameter-Efficient Fine-Tuning for Few-Shot 3D Medical Image Segmentation

  Son Thai Ly, Hien V. Nguyen · 9 mars 2026

  Adapting foundation models to new clinical sites remains challenging in practice. Domain shift and scarce annotations must be handled by experts, yet many clinical groups do not have ready access to skilled AI engineers to tune adapter designs and training recipes. As a result, adaptation cycles can…

• CLoPA: Continual Low Parameter Adaptation of Interactive Segmentation for Medical Image Annotation

  Parhom Esmaeili, Chayanin Tangwiriyasakul, Eli Gibson, Sebastien Ourselin, M. Jorge Cardoso · 9 mars 2026

  Interactive segmentation enables clinicians to guide annotation, but existing zero-shot models like nnInteractive fail to consistently reach expert-level performance across diverse medical imaging tasks. Because annotation campaigns produce a growing stream of task-specific labelled data, online ada…

• Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

  Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen, Jiaming Liu, Longchao Liu, Dave Van Veen, Syed Jamal Safdar Gardezi, Hongkun Yu, Magdalini Paschali, Zhihong Chen, Jean-Benoit Delbrouck, Eduardo Reis, Robbie Holland, Cesar Truyts, Christian Bluethgen, Yufu Wu, Long Lian, Malte Engmann Kjeldskov Jensen, Sophie Ostmeier, Maya Varma, Jeya Maria Jose Valanarasu, Zhongnan Fang, Zepeng Huo, Zaid Nabulsi, Diego Ardila, Wei-Hung Weng, Edson Amaro Junior, Neera Ahuja, Jason Fries, Nigam H. Shah, Greg Zaharchuk, Marc Willis, Adam Yala, Andrew Johnston, Robert D. Boutin, Andrew Wentland, Curtis P. Langlotz, Jason Hom, Sergios Gatidis, Akshay S. Chaudhari · 5 mars 2026

  The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previous state-of-the-art approaches for automated analysis leverage vision-language models (VLMs) that jointly model images an…

• Efficient Conformal Volumetry for Template-Based Segmentation

  Matt Y. Cheung, Ashok Veeraraghavan, Guha Balakrishnan · 3 mars 2026

  Template-based segmentation, a widely used paradigm in medical imaging, propagates anatomical labels via deformable registration from a labeled atlas to a target image, and is often used to compute volumetric biomarkers for downstream decision-making. While conformal prediction (CP) provides finite-…

• Less is More: AMBER-AFNO -- a New Benchmark for Lightweight 3D Medical Image Segmentation

  Andrea Dosi, Semanto Mondal, Rajib Chandra Ghosh, Massimo Brescia, Giuseppe Longo · 2 mars 2026

  We adapt the remote sensing-inspired AMBER model from multi-band image segmentation to 3D medical datacube segmentation. To address the computational bottleneck of the volumetric transformer, we propose the AMBER-AFNO architecture. This approach uses Adaptive Fourier Neural Operators (AFNO) instead …

• Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?

  Ayush Roy, Samin Enam, Jun Xia, Won Hwa Kim, Vishnu Suresh Lokhande · 25 février 2026

  Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown to enhance model performance, they are not without complications. Specif…

• Regularized Top-$k$: A Bayesian Framework for Gradient Sparsification

  Ali Bereyhi, Ben Liang, Gary Boudreau, Ali Afana · 17 février 2026

  Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceeds a certain level. The accumulation essentially behaves as a scaling of the learning rate for the selected entries. Alth…

• PLESS: Pseudo-Label Enhancement with Spreading Scribbles for Weakly Supervised Segmentation

  Yeva Gabrielyan (Akian College of Science and Engineering, American University of Armenia, Yerevan, Armenia), Varduhi Yeghiazaryan (Akian College of Science and Engineering, American University of Armenia, Yerevan, Armenia), Irina Voiculescu (Department of Computer Science, University of Oxford, Oxford, UK) · 13 février 2026

  Weakly supervised learning with scribble annotations uses sparse user-drawn strokes to indicate segmentation labels on a small subset of pixels. This annotation reduces the cost of dense pixel-wise labeling, but suffers inherently from noisy and incomplete supervision. Recent scribble-based approach…

• Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation

  Carolina Br\'as, Soufiane Ben Haddou, Thijs P. Kuipers, Laura Alvarez-Florez, R. Nils Planken, Fleur V. Y. Tjong, Connie Bezzina, Ivana I\v{s}gum · 13 février 2026

  The anisotropic nature of short-axis (SAX) cardiovascular magnetic resonance imaging (CMRI) limits cardiac shape analysis. To address this, we propose to leverage near-isotropic, higher resolution computed tomography angiography (CTA) data of the heart. We use this data to train a single neural impl…

• Generalized Robust Adaptive-Bandwidth Multi-View Manifold Learning in High Dimensions with Noise

  Xiucai Ding, Chao Shen, Hau-Tieng Wu · 12 février 2026

  Multiview datasets are common in scientific and engineering applications, yet existing fusion methods offer limited theoretical guarantees, particularly in the presence of heterogeneous and high-dimensional noise. We propose Generalized Robust Adaptive-Bandwidth Multiview Diffusion Maps (GRAB-MDM), …