Medical Image Segmentation Techniques

Últimos artículos

• 3D Transport-based Morphometry (3D-TBM) for medical image analysis

  Hongyu Kan, Kristofor Pas, Ivan Medri, Naqib Sad Pathan, Natasha Ironside, Shinjini Kundu, Jingjia He, Gustavo Kunde Rohde · 10 de febrero de 2026

  Transport-Based Morphometry (TBM) has emerged as a new framework for 3D medical image analysis. By embedding images into a transport domain via invertible transformations, TBM facilitates effective classification, regression, and other tasks using transport-domain features. Crucially, the inverse ma…

• Efficient Brain Extraction of MRI Scans with Mild to Moderate Neuropathology

  Hjalti Thrastarson, Lotta M. Ellingsen · 10 de febrero de 2026

  Skull stripping magnetic resonance images (MRI) of the human brain is an important process in many image processing techniques, such as automatic segmentation of brain structures. Numerous methods have been developed to perform this task, however, they often fail in the presence of neuropathology an…

• Neural Implicit 3D Cardiac Shape Reconstruction from Sparse CT Angiography Slices Mimicking 2D Transthoracic Echocardiography Views

  Gino E. Jansen, Carolina Br\'as, R. Nils Planken, Mark J. Schuuring, Berto J. Bouma, Ivana I\v{s}gum · 6 de febrero de 2026

  Accurate 3D representations of cardiac structures allow quantitative analysis of anatomy and function. In this work, we propose a method for reconstructing complete 3D cardiac shapes from segmentations of sparse planes in CT angiography (CTA) for application in 2D transthoracic echocardiography (TTE…

• Towards Segmenting the Invisible: An End-to-End Registration and Segmentation Framework for Weakly Supervised Tumour Analysis

  Budhaditya Mukhopadhyay, Chirag Mandal, Pavan Tummala, Naghmeh Mahmoodian, Andreas N\"urnberger, Soumick Chatterjee · 6 de febrero de 2026

  Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively invisible on intra-operative CT due to minimal contrast between pathological and healthy tissue. This work investigates the feasibility of cross-modali…

• Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations

  Zhijian Yang, Noel DSouza, Istvan Megyeri, Xiaojian Xu, Amin Honarmandi Shandiz, Farzin Haddadpour, Krisztian Koos, Laszlo Rusko, Emanuele Valeriano, Bharadwaj Swaninathan, Lei Wu, Parminder Bhatia, Taha Kass-Hout, Erhan Bas · 4 de febrero de 2026

  Magnetic Resonance Imaging is a critical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity hinder scalable, generalizable machine learning. Although foundation models have revolutionized language and vision tasks, their application to MRI remains constrained b…

• Structure-constrained Language-informed Diffusion Model for Unpaired Low-dose Computed Tomography Angiography Reconstruction

  Genyuan Zhang, Zihao Wang, Zhifan Gao, Lei Xu, Zhen Zhou, Haijun Yu, Jianjia Zhang, Xiujian Liu, Weiwei Zhang, Shaoyu Wang, Huazhu Fu, Fenglin Liu, Weiwen Wu · 29 de enero de 2026

  The application of iodinated contrast media (ICM) improves the sensitivity and specificity of computed tomography (CT) for a wide range of clinical indications. However, overdose of ICM can cause problems such as kidney damage and life-threatening allergic reactions. Deep learning methods can genera…

• Beyond the noise: intrinsic dimension estimation with optimal neighbourhood identification

  Antonio Di Noia, Iuri Macocco, Aldo Glielmo, Alessandro Laio, Antonietta Mira · 28 de enero de 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…

• Beyond Known Fakes: Generalized Detection of AI-Generated Images via Post-hoc Distribution Alignment

  Li Wang, Wenyu Chen, Xiangtao Meng, Zheng Li, Shanqing Guo · 19 de enero de 2026

  The rapid proliferation of highly realistic AI-generated images poses serious security threats such as misinformation and identity fraud. Detecting generated images in open-world settings is particularly challenging when they originate from unknown generators, as existing methods typically rely on m…

• A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI

  Wenhui Chu, Aobo Jin, Hardik A. Gohel · 6 de enero de 2026

  This research aims to develop a novel deep learning network, GBU-Net, utilizing a group-batch-normalized U-Net framework, specifically designed for the precise semantic segmentation of the left ventricle in short-axis cine MRI scans. The methodology includes a down-sampling pathway for feature extra…

• Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI

  Wenhui Chu, Nikolaos V. Tsekos · 5 de enero de 2026

  Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normaliza…

• Multimodal Diffeomorphic Registration with Neural ODEs and Structural Descriptors

  Salvador Rodriguez-Sanz, Monica Hernandez · 30 de diciembre de 2025

  This work proposes a multimodal diffeomorphic registration method using Neural Ordinary Differential Equations (Neural ODEs). Nonrigid registration algorithms exhibit tradeoffs between their accuracy, the computational complexity of their deformation model, and its proper regularization. In addition…

• Unified Brain Surface and Volume Registration

  S. Mazdak Abulnaga, Andrew Hoopes, Malte Hoffmann, Robin Magnet, Maks Ovsjanikov, Lilla Z\"ollei, John Guttag, Bruce Fischl, Adrian Dalca · 24 de diciembre de 2025

  Accurate registration of brain MRI scans is fundamental for cross-subject analysis in neuroscientific studies. This involves aligning both the cortical surface of the brain and the interior volume. Traditional methods treat volumetric and surface-based registration separately, which often leads to i…

• Magnification-Aware Distillation (MAD): A Self-Supervised Framework for Unified Representation Learning in Gigapixel Whole-Slide Images

  Mahmut S. Gokmen, Mitchell A. Klusty, Peter T. Nelson, Allison M. Neltner, Sen-Ching Samson Cheung, Thomas M. Pearce, David A Gutman, Brittany N. Dugger, Devavrat S. Bisht, Margaret E. Flanagan, V. K. Cody Bumgardner · 18 de diciembre de 2025

  Whole-slide images (WSIs) contain tissue information distributed across multiple magnification levels, yet most self-supervised methods treat these scales as independent views. This separation prevents models from learning representations that remain stable when resolution changes, a key requirement…

• Adaptive Plane Reformatting for 4D Flow MRI using Deep Reinforcement Learning

  Javier Bisbal, Julio Sotelo, Maria I Vald\'es, Pablo Irarrazaval, Marcelo E Andia, Julio Garc\'ia, Jos\'e Rodriguez-Palomarez, Francesca Raimondi, Cristi\'an Tejos, Sergio Uribe · 2 de diciembre de 2025

  Background and Objective: Plane reformatting for four-dimensional phase contrast MRI (4D flow MRI) is time-consuming and prone to inter-observer variability, which limits fast cardiovascular flow assessment. Deep reinforcement learning (DRL) trains agents to iteratively adjust plane position and ori…

• Multi-view diffusion geometry using intertwined diffusion trajectories

  Gwendal Debaussart-Joniec (CB), Argyris Kalogeratos (CB) · 2 de diciembre de 2025

  This paper introduces a comprehensive unified framework for constructing multi-view diffusion geometries through intertwined multi-view diffusion trajectories (MDTs), a class of inhomogeneous diffusion processes that iteratively combine the random walk operators of multiple data views. Each MDT defi…

• MedCondDiff: Lightweight, Robust, Semantically Guided Diffusion for Medical Image Segmentation

  Ruirui Huang, Jiacheng Li · 2 de diciembre de 2025

  We introduce MedCondDiff, a diffusion-based framework for multi-organ medical image segmentation that is efficient and anatomically grounded. The model conditions the denoising process on semantic priors extracted by a Pyramid Vision Transformer (PVT) backbone, yielding a semantically guided and lig…

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

  Roman Naeem, David Hagerman, Jennifer Alv\'en, Fredrik Kahl · 27 de noviembre de 2025

  Tubular trees, such as blood vessels and lung airways, are essential for material transport within the human body. Accurately detecting their centerlines with correct tree topology is critical for clinical tasks such as diagnosis, treatment planning, and surgical navigation. In these applications, m…

• A Physics-Informed Loss Function for Boundary-Consistent and Robust Artery Segmentation in DSA Sequences

  Muhammad Irfan, Nasir Rahim, Khalid Mahmood Malik · 26 de noviembre de 2025

  Accurate extraction and segmentation of the cerebral arteries from digital subtraction angiography (DSA) sequences is essential for developing reliable clinical management models of complex cerebrovascular diseases. Conventional loss functions often rely solely on pixel-wise overlap, overlooking the…

• CORE - A Cell-Level Coarse-to-Fine Image Registration Engine for Multi-stain Image Alignment

  Esha Sadia Nasir, Behnaz Elhaminia, Mark Eastwood, Catherine King, Owen Cain, Lorraine Harper, Paul Moss, Dimitrios Chanouzas, David Snead, Nasir Rajpoot, Adam Shephard, Shan E Ahmed Raza · 26 de noviembre de 2025

  Accurate and efficient registration of whole slide images (WSIs) is essential for high-resolution, nuclei-level analysis in multi-stained tissue slides. We propose a novel coarse-to-fine framework CORE for accurate nuclei-level registration across diverse multimodal whole-slide image (WSI) datasets.…

• CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models

  Karim Kadry, Shoaib Goraya, Ajay Manicka, Abdalla Abdelwahed, Naravich Chutisilp, Farhad Nezami, Elazer Edelman · 26 de noviembre de 2025

  Generative models of 3D cardiovascular anatomy can synthesize informative structures for clinical research and medical device evaluation, but face a trade-off between geometric controllability and realism. We propose CardioComposer: a programmable, inference-time framework for generating multi-class…

• Enhancing Visual Feature Attribution via Weighted Integrated Gradients

  Kien Tran Duc Tuan, Tam Nguyen Trong, Son Nguyen Hoang, Khoat Than, Anh Nguyen Duc · 21 de noviembre de 2025

  Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential. A key limitation of IG is its sensitivity to the choice of baseline (reference) images. Multi-baseline extensions such as Exp…

• AtlasMorph: Learning conditional deformable templates for brain MRI

  Marianne Rakic, Andrew Hoopes, S. Mazdak Abulnaga, Mert R. Sabuncu, John V. Guttag, Adrian V. Dalca · 18 de noviembre de 2025

  Deformable templates, or atlases, are images that represent a prototypical anatomy for a population, and are often enhanced with probabilistic anatomical label maps. They are commonly used in medical image analysis for population studies and computational anatomy tasks such as registration and segme…

• A Multicollinearity-Aware Signal-Processing Framework for Cross-$\beta$ Identification via X-ray Scattering of Alzheimer's Tissue

  Abdullah Al Bashit, Prakash Nepal, Lee Makowski · 18 de noviembre de 2025

  X-ray scattering measurements of in situ human brain tissue encode structural signatures of pathological cross-$\beta$ inclusions, yet systematic exploitation of these data for automated detection remains challenging due to substrate contamination, strong inter-feature correlations, and limited samp…

• Convolutional Model Trees

  William Ward Armstrong · 18 de noviembre de 2025

  A method for creating a forest of model trees to fit samples of a function defined on images is described in several steps: down-sampling the images, determining a tree's hyperplanes, applying convolutions to the hyperplanes to handle small distortions of training images, and creating forests of mod…

• CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation

  Max Curie, Paulo da Costa · 27 de octubre de 2025