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
  J. 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…

• What Drives Purchase Intention and Brand Loyalty in Affordable Luxury Fashion? Evidence from Coach Consumers
  Angela Audrey Sutanto, Dudi Anandya · Ubaya Repository (Universit... · Oct 1, 2026

  In the era of globalization and digitalization, Indonesia's luxury fashion industry has continued to grow, including the affordable luxury segment, creating a need for brands to understand the factors influencing consumers’ purchase intenti…

• Spectral Adapters for Segment Anything Model-based Segmentation of Colorectal Liver Metastases in Computed Tomography
  Ramtin Mojtahedi, Mohammad Hamghalam, Jacob J. Peoples, Natalie Gangai et al. · arXiv · Sep 10, 2026

  Accurate segmentation of colorectal liver metastases (CRLM) in contrast-enhanced computed tomography (CT) is important for response assessment, surgical planning, and follow-up. We propose two parameter-efficient spectral adapters for the S…

• UBone3D: Physics-Rectified Conditional Flow Matching for Anatomical 3D Shape Completion from Ultrasound
  Weiying Chen, Yuchong Gao, Siyuan Li, Marek Reformat et al. · arXiv · Sep 10, 2026

  Three-dimensional ultrasound (US) is a safe, radiation-free complementary modality to CT and X-rays for longitudinal monitoring, yet its segmentation-derived partial point clouds are extremely artifact-laden. Consequently, it is challenging…

• Pre- and Post-Treatment Brain Metastases Segmentation Using nnU-Net with Post-Processing for BraTS 2026
  Haobin Liu, Xin Wang · arXiv · Sep 10, 2026

  Brain metastases exhibit high inter-lesion variability in size, enhancement pattern, and post-treatment appearance, making volumetric segmentation of both pre- and post-treatment cases the central challenge of the BraTS 2026 Task 1 (Brain M…

• BruNet: A Cross-Domain Transfer Framework for Bruise Segmentation
  Qiming Wang, Richard J. Motley, Ebube E. Obi, Xianfang Sun et al. · arXiv · Sep 10, 2026

  Segmenting bruises is a challenging task in medical imaging due to limited data and annotations, diffuse boundaries, and highly variable appearance. In this work, we propose BruNet, a segmentation framework that combines a ViT-based visual …

• R4Tun: LLM-guided adaptive segmental tunnel lining segmentation in point clouds
  Xinghui Tao, Zehao Ye, Guangming Wang, Jelena Ninić et al. · arXiv · Sep 10, 2026

  Automated inspection of segmental tunnel linings requires adaptive segmentation from 3D point clouds, yet expert-tuned pipelines often degrade when tunnel conditions vary. This paper presents R4Tun, a large language model (LLM)-driven adapt…

• Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation
  Siddharth Gupta, Jitin Singla · arXiv · Sep 9, 2026

  In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a pr…

• Enhanced Deformable Convolution with Center-invariant Offset and Edge-aware Mask
  Yixiao Li, Xiaoyuan Yang, Jin Jiang, Minghao Zou et al. · arXiv · Sep 9, 2026

  Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exceptional capabilities in dynamic spatial modeling. However, due to the dense deformable o…

• Beyond Weak Labels: Prompt-Guided Local Refinement for Weakly Supervised Water Segmentation in High-Resolution Multispectral Imagery
  Muhammad Farhan Humayun, Mohammad Imangholiloo, Afifah Shah, Tomi Westerlund et al. · arXiv · Sep 9, 2026

  High-resolution water mapping supports environmental monitoring and related applications, but accurate pixel-level labels are difficult and costly to produce. Official hydrographic vectors provide scalable weak supervision, but they contain…

• Geometry Without Coordinates: LiDAR Diffusion as a 3D Feature Bridge
  Samed Doğan, Nico Leuze, Alfred Schöttl · arXiv · Sep 9, 2026

  Transferring the rich priors of large 2D foundation models to sparse 3D LiDAR remains challenging, as training native 3D foundation models at comparable scale is limited by data and annotation scarcity. We introduce a LiDAR-conditioned diff…

• When Fusion Fails: Corruption-Aware Rebalanced Fusion for Multi-Modal Medical Image Segmentation
  Yuchen Pei, Xiaoyu Hu, Yixiong Zou, Dingwen Hu et al. · arXiv · Sep 9, 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-in…

• From Few-Shot Segmentation to Clinician-in-the-Loop Medical Image Analysis
  Yazhou Zhu · arXiv · Sep 9, 2026

  Few-shot medical image segmentation (FSMIS) seeks to delineate unseen structures from a small support set, but its standard formulation fixes task-defining evidence before inference. This assumption is fragile when query cases exhibit acqui…

• Medical AI Encodes a "Feeling of Error": Verifying Cancer Segmentation via Internal Concepts
  Mengmeng Ma, Yunxiang Peng, Tang Li, Lu Lin et al. · arXiv · Sep 8, 2026

  Cancer segmentation models can fail silently, generating plausible but incorrect masks that risk missed findings or unnecessary biopsies. A critical question arises: Do AI models "know" when they are wrong, and if so, can we use the signal …

• SeGDeP: Semantic- and Geometric-Aware Decoupled Prompts for Reasoning Segmentation
  Linnan Zhao, Xu Liu, Lingling Li, Licheng Jiao et al. · arXiv · Sep 8, 2026

  Reasoning segmentation converts an implicit linguistic conclusion into a precise mask, requiring both semantic identification and spatial grounding. Existing MLLM-segmenter interfaces either use a special trigger or compress both signals in…

• Multimodal automated diagnosis of lymphovascular invasion in breast cancer on contrast-enhanced MRI: ResUNet + + segmentation and transformer-based classification
  Junyu Lin, Zichang Ma, Yuxi Tao, Yun Liang et al. · BMC Medical Imaging · Sep 8, 2026

  Abstract Objectives To develop and evaluate an automated, multimodal Transformer model for preoperative prediction of lymphovascular invasion (LVI) in invasive breast cancer using contrast-enhanced MRI. Materials and Methods A retrospective…

• Assessment of threshold-based infarct core and penumbra segmentations from spectral CT angiography iodine density maps
  Joris Vromans, Edwin Bennink, Jan Willem Dankbaar, B K Velthuis et al. · Scientific Reports · Sep 8, 2026

  Abstract CT perfusion (CTP) can differentiate infarct core (IC), penumbra (PEN), and healthy area (HA) in acute ischemic stroke. Quantitative iodine density maps from spectral CT angiography (CTA) may offer a lower-dose alternative. This st…

• BRAIN TUMOR SEGMENTATION OF MRI SEQUENCES (T1, T2, T1CE, FLAIR) USING BRATS DATASET
  Lovedeep Kaur, Parminder Singh, Naveen Dhillon · International Journal of Co... · Sep 6, 2026

  Brain tumor segmentation from Magnetic Resonance Imaging (MRI) is an important task in computer-aided diagnosis because accurate identification of tumor regions supports clinical assessment and treatment planning. However, the complex struc…

• Explainable Binary Classification of Separable Shape Ensembles
  Zachary Grey, Nicholas Fisher, Andrew Glaws · Journal of Mathematical Ima... · Sep 6, 2026

  Abstract Scientists, engineers, biologists, and technology specialists universally leverage image segmentation to extract shape ensembles containing many thousands of curves representing patterns in observations and measurements. These larg…

• Fspgd: rethinking black-box attacks on semantic segmentation
  Eun-Sol Park, Miso Park, Yong-Goo Shin · Machine Vision and Applicat... · Sep 4, 2026
• Efficient Semantic Understanding from Digital Foveation
  Caterina Caccavella, Vittorio Fra, Andreas Ziegler, Giulia D'Angelo et al. · arXiv · Sep 3, 2026

  Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of scene complexity or task relevance. Inspired by biological vision, we investigate whether semantic understanding can be achieved …

• ENEAS: Embedding-guided Neural Ensemble for Adaptive Segmentation
  Javier del Pino, Salvador Rodríguez, Alejandro Garabito, Javier Álvarez et al. · arXiv · Sep 3, 2026

  We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-promptable segmentation models, including the latest foundation models such as SAM 3, still suffer from temporal hallucinations, spatial …

• NucGen3D: A synthetic framework for large-scale 3D nuclear segmentation with open-source training data and models
  Emma Grandgirard, Theotime Dmitrasinovic, Corinne Barreau, Coralie Sengenès et al. · Computers in Biology and Me... · Sep 3, 2026

  A bstract Robust nuclear segmentation in 3D microscopy images is a critical yet unresolved challenge in quantitative cell biology, hindered by the scarcity and variability of annotated volumetric datasets. Because such data are difficult to…

• Finite Sample Complexity Analysis of Binary Segmentation
  Toby Dylan Hocking · Algorithms · Sep 3, 2026

  Binary segmentation is the classic greedy algorithm which recursively splits a sequential data set by optimizing some loss or likelihood function. Binary segmentation is widely used for changepoint detection in data sets measured over space…

• PlantC2USeg: Cross-Scale Consistent Pre-Training for Few-Shot Unified Plant Point Cloud Segmentation
  Yu Tian, Xintong Jiang, Jan Franklin Adamowski, Shiv O. Prasher et al. · arXiv · Sep 2, 2026

  Modern crop breeding demands precise organ-level analysis for trait quantification, making plant point cloud segmentation (PPCS) increasingly important. However, conventional deep learning approaches rely heavily on densely annotated datase…

• Query Rewriting for Complex Object Segmentation in 4D Gaussian Representations
  Thanh-Khoi Nguyen, Thien-Phuc Tran, Minh-Triet Tran · arXiv · Sep 2, 2026

  Recent 4D Gaussian representation frameworks have demonstrated strong performance in language-guided dynamic scene understanding. However, these methods remain highly sensitive to verbose and narrative-style queries that contain noisy conte…

• Characterizing Text Branch Sensitivity in Medical Vision-Language Segmentation via Evidence Decoupling
  Ziquan Liu, Zhewei Zhu, Xuyang Shi · arXiv · Sep 2, 2026

  Pretrained vision-language models (VLMs) have shown promising performance in medical image segmentation by incorporating clinical text. However, it remains unclear how much textual information actually contributes to pixel-level predictions…

• Generalizable Brain Tumor Segmentation with Self-Training and Tumor-Aware Deformations
  Henrique Zan Grande, Jeovane Honorio Alves, Rayson Laroca, Andre Gustavo Hochuli · arXiv · Sep 2, 2026

  This work presents an approach to the Generalizability Across Tumors (BraTS-GoAT) task of the BraTS 2026 Challenge, which focuses on robust segmentation of brain tumor sub-regions across a heterogeneous patient population. The proposed meth…

• Enhancing Bone Marrow Lesion Segmentation Through Dual-Channel Deep Neural Networks and Test-Time Augmentation
  S. N. Qin, Hetali Tank, Qi Wang, Ke Wang et al. · Electronics · Sep 2, 2026

  Bone marrow lesion (BML) volume is an essential biomarker for understanding knee osteoarthritis (KOA). However, automatic BML segmentation remains challenging due to the irregular shapes and indistinct boundaries of these lesions in knee ma…

• Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation
  Philip Gutberlet, Aron Kirchhoff, Eike Bolmer, Philipp Schmidt et al. · Scientific Reports · Sep 2, 2026

  Abstract Evaluating hand and wrist radiographs is essential in pediatric endocrinology and clinical genetics, particularly for the assessment of suspected skeletal anomalies. In this study, we present Auto-Bone-Caliper, an automated system …