Latest Image Segmentation Research Papers

The newest Image Segmentation papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Image Segmentation so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• From Sparse to Dense: Label-Efficient Weakly Supervised Segmentation for Images and Videos
  Jian Wang · University of Liverpool · Jan 1, 2027

  Obtaining high-quality annotated data has become a primary bottleneck for training deep learning models, particularly for dense prediction tasks like semantic segmentation and video salient object segmentation. The demand for meticulous, pi…

• KPIs 2024 challenge: Advancing glomerular segmentation from patch- to slide-level.
  Ruining Deng, Tianyuan Yao, Yucheng Tang, Junlin Guo et al. · PubMed · Nov 1, 2026

  Chronic kidney disease (CKD) is a major global health issue, affecting over 10% of the population and causing significant mortality. While kidney biopsy remains the gold standard for CKD diagnosis and treatment, the lack of comprehensive be…

• Prediction of humeral torsion from proximal humerus computed tomography is enabled by three-dimensional estimation of the transepicondylar axis using deep learning.
  Alexandra Oswald, Hanspeter Hess, Michael Schär, Matthias A. Zumstein et al. · PubMed · Nov 1, 2026

  Background: Humeral torsion is an important factor for reverse total shoulder arthroplasty planning, yet its measurement requires elbow imaging frequently absent from clinical computed tomography (CT) scans. We hypothesized that a deep lear…

• A Generalized Software Framework for Consolidation of Radiation Therapy Planning and Delivery Data From Diverse Data Sources.
  Yasin Abdulkadir, Justin Hink, Peter Boyle, Dishane Luximon et al. · PubMed · Nov 1, 2026

  Purpose: Large-scale radiation therapy data sets drive predictive modeling, automated segmentation and planning, and personalized treatment, yet remain fragmented because treatment planning (TPS) and record-and-verify (R&V) systems differ, …

• Machine Learning and Deep Learning for Brain Tumor Prediction and Segmentation in MRI: A Systematic Review of Methods, Datasets and Open Challenges
  Namrata Vijayvargiya, Nirupma Singh · Zenodo (CERN European Organ... · Oct 30, 2026

  Magnetic resonance imaging (MRI) is the reference modality for brain tumor diagnosis, yet manual delineation remains slow, expertise-dependent and subject to inter-observer variability. Over the last four years the literature has shifted de…

• Machine Learning and Deep Learning for Brain Tumor Prediction and Segmentation in MRI: A Systematic Review of Methods, Datasets and Open Challenges
  Namrata Vijayvargiya, Nirupma Singh · Zenodo (CERN European Organ... · Oct 30, 2026

  Magnetic resonance imaging (MRI) is the reference modality for brain tumor diagnosis, yet manual delineation remains slow, expertise-dependent and subject to inter-observer variability. Over the last four years the literature has shifted de…

• Beyond Ground Truth: A Heuristic Unsupervised Quality Assurance Engine for Diagnosing Calibrational Blindness in Dental Segmentation
  Fatematuj Johura Mim · arXiv · Oct 29, 2026

  In real-world clinical settings, dental X-ray segmentation using deep learning methods suffers from reliability issues. Even when the segmentation quality is bad, deep learning models show high confidence in their predictions (calibrational…

• AVPS-360: A Spatial Audio-Visual Benchmark for Instance Segmentation and Tracking of Sound-Producing Persons
  Yifu Sun, Parthasaarathy Sudarsanam, Tuomas Virtanen, Archontis Politis · arXiv · Oct 29, 2026

  Audio-visual instance segmentation (AVIS) segments and tracks individual sound-producing objects in videos. Existing benchmarks predominantly use monaural audio, which lacks inter-channel cues for spatially distinguishing concurrent sound s…

• Self-training for cross-centre coronary vessel segmentation in X-ray angiography using pre-contrast frame subtraction
  Danial Moafi, Aida Moafi, Mostafa Aryafar, Ashkan Sami et al. · arXiv · Oct 29, 2026

  Deep learning models for coronary vessel segmentation in X-ray angiography often perform poorly on data from centres that were not represented in training. Apart from differences in equipment, the labels of the largest public dataset (ARCAD…

• Hierarchical Step Decomposition for Training-free Online Video Step Grounding
  Long Zhou, Luca Zanella, Paolo Rota, Massimiliano Mancini et al. · arXiv · Oct 29, 2026

  Given a set of procedural steps, Video Step Grounding (VSG) aims to identify which steps occur in a video and localize when they are performed. Existing methods are either offline, requiring access to the full video before making prediction…

• SynthFaces: Balanced Large Scale Human Dataset
  Sergey Karpukhin, Denis Bobkov, Kirill Morozov, Aibek Alanov · arXiv · Oct 29, 2026

  The pursuit of fair and equitable AI in human-centric tasks is critically hindered by the inherent biases and privacy risks of real-world datasets, which offer imbalanced representations of identity and often contain sensitive personal info…

• Container-surface-guided dual-task learning for defect detection in complex port scenes
  Mengyao Zhen, Weidong Zhao, Xianhui Liu, Ning Jia · Intelligence & Robotics · Oct 28, 2026

  Automatic container defect inspection in complex port environments is hindered by substantial scale variation and defect-like interference from non-container regions. This article presents CSG-YOLO, a container-surface-guided dual-task fram…

• Zero-Shot Multimodal Event Spotting in Soccer Broadcasts
  Sadia Kamal, Mahmudul Hasan, Tony Braskich · arXiv · Oct 27, 2026

  Automatic soccer event spotting supports highlight generation, video indexing, and sports analytics, but existing methods depend on large annotated datasets and soccer-specific training. We present a zero-shot framework that detects, classi…

• Segmentation Confidence for Arbitrary CNNs
  Baris Oguz, Xing Yao, Claudia Tawil, Alison Pouch et al. · arXiv · Oct 27, 2026

  Convolutional neural networks (CNN) are widely used for medical image segmentation. However, they lack an inherent measure of confidence. Existing methods for estimating prediction uncertainty often require a custom network architecture and…

• TRUCO: A Scalable Lock-Free Algorithm for Parallel Contour Extraction
  Rafael Munoz-Salinas, Francisco J. Romero-Ramirez, Manuel J. Marin-Jimenez · arXiv · Oct 27, 2026

  Transforming binary masks into geometric contours remains a critical step in many computer vision pipelines, particularly for post-processing high-resolution segmentation outputs. Yet, standard algorithms such as the Suzuki-Abe method are l…

• HDVS: semi-supervised semantic segmentation via heterogeneous dual-branch voting supervision
  Yongqi Shan, Yunzhi Zhuge, Huchuan Lu · arXiv · Oct 27, 2026

  Semi-supervised semantic segmentation, which aims to utilize large volumes of unlabeled images to achieve accurate segmentation results with fewer human annotations, has attracted increasing attention. Prior methods were primarily based on …

• TopoField: Technical Report for ECCV 2026 SceneFun3D Challenge
  Zhiwei Chen, Zhiheng Fu, Zixu Li, Mingyu Zhang et al. · arXiv · Oct 27, 2026

  Functional segmentation requires locating small interactive parts, such as knobs, buttons, and sockets, in indoor point clouds and returning confidence-ranked instance masks. A point-wise semantic predictor produces a continuous probability…

• VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
  Richard Fechner, Peer Neubert · arXiv · Oct 26, 2026

  Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-of-the-art VPR and SLAM approaches excel in urban and feature-rich natural environments, the…

• S$^2$-RL: Sample-Set Dual Reinforcement Learning for Generative Semantic Segmentation Dataset Distillation
  Haoyu Wang, Fei Zhou, Qingqing Qiu, Lei Zhang et al. · arXiv · Oct 26, 2026

  While dataset distillation has witnessed significant advances in image classification, its extension to dense prediction tasks such as semantic segmentation is plagued by two core bottlenecks: bi-level optimization-based approaches suffer f…

• The effect of urban density on stock returns: evidence from satellite imagery and firms in the United States
  Mahsa Memarian, Carles Vergara‐Alert, Sara Benetti · Journal of Economics and Fi... · Oct 26, 2026

  Abstract We study how the urban density of the areas where firms are located impacts their stock returns. Using high-resolution satellite images and image segmentation algorithms, we develop measures of building density for the most populat…

• Resolving primitive-sharing ambiguity in long-tailed TLS-based industrial MEP point cloud segmentation via spatial context constraints
  Chao Yin, Qing Han, Zhiwei Hou, Yue Liu et al. · Advanced Engineering Inform... · Oct 25, 2026
• A Computer Vision-Based Framework for Multilevel Egg Fertility Classification Using L*a*b* Color Segmentation, Wavelet Texture Features, and Support Vector Machine
  Sriyadi, Jufriadif Na’am, Mochamad Wahyudi, Sumanto · Journal of Information Syst... · Oct 24, 2026

  Egg fertility assessment is essential for improving hatchery efficiency, reducing incubation costs, and minimizing resource waste caused by infertile eggs. This study aims to develop a computer vision-based framework for three-level egg fer…

• Beyond subjective grading: objective quantification of corneal neovascularization by mathematical modeling of slit-lamp photographs after Holoclar therapy – a proof-of-concept study
  Monika Horáčková, Eva Vlková, Graziella Pellegrini, Cecília Marinová et al. · Zenodo (CERN European Organ... · Oct 24, 2026

  Dataset Description This dataset contains longitudinal clinical ophthalmic images acquired from five patients with moderate to severe limbal stem cell deficiency (LSCD) caused by chemical or thermal ocular injuries who underwent Holoclar tr…

• Cross-Architectural Feature Fusion for Coronary Artery Stenosis Detection in X-ray Angiography
  Haobo Zhu, Mojtaba Lashgari, Jason Chai, Giovanni Luigi De Maria et al. · arXiv · Oct 23, 2026

  Coronary artery disease remains a leading cause of mortality, with stenosis detection from invasive X-ray Coronary Angiography (XCA) critical for diagnosis. Manual interpretation is time-consuming, subjective, and variable, especially under…

• DriveOSS: Dataset and Benchmark for 3D Anomaly Segmentation and Open-set Semantic Segmentation in Autonomous Driving
  Zhiran Yan, Savankumar Bhanderi, Sourabh Lolge, Rui Song et al. · arXiv · Oct 23, 2026

  Recent advances in 3D semantic segmentation have significantly improved scene understanding in autonomous driving. However, existing methods are typically developed under a closed-set assumption and evaluated under standard driving conditio…

• Hair Segmentation and Skin Characterization on Macroscopic Dermatological Images for Laser Treatment Parameterization
  Alex Navarro Navarro, Marc Arriaga Hidalgo, Carlos Moreno Rubio, Maxime Talarmain et al. · arXiv · Oct 23, 2026

  Current light-based devices, such as laser hair removal, intense pulsed light (IPL), vascular laser therapy, and dermatologic phototherapy, are widely used in clinical dermatology but rely heavily on subjective manual parameterization or un…

• Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation
  Gabriel Oliveira-Stahl, Anna Schroder, James Moggridge, Hamza A. Salhab et al. · arXiv · Oct 22, 2026

  Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer's disease and epilepsy. While numerous automated segmentation methods exist, their clinical adoption remains limi…

• MoG-VLM: Efficient Video Anomaly Detection through Motion-Guided Vision-Language Models
  Khaled Waleed Dawoud, Muhammad Zaigham Zaheer, Khan Muhammad, Muhammad Haris Khan · arXiv · Oct 22, 2026

  Vision-Language Models (VLMs) have shown strong potential for video anomaly detection, but dense frame-level inference makes them impractical for real-time deployment. Existing acceleration commonly relies on uniform sampling, which ignores…

• WeVisOne-VL: Unified Visual Perception from Objects to Pixels
  Tianming Liang, Junyi Pan, Yuxi Li, Canmiao Fu et al. · arXiv · Oct 22, 2026

  Existing vision-language models (VLMs) remain unreliable for fine-grained visual perception. In this report, we present WeVisOne-VL (WeChat Vision All-in-One), a powerful VLM that unifies object-level and pixel-level perception through stru…

• Lightweight Frame Compensation for Real-Time Polyp Segmentation under Frame Skipping
  Weihao Gao · arXiv · Oct 22, 2026

  Real-time polyp segmentation in computer-assisted colonoscopy is hampered by the difficulty of deploying high-accuracy models under strict latency budgets. Frame skipping, which performs inference every K frames and reuses or interpolates t…