Recent Advances in Medical Imaging Segmentation: A Survey

• URL: http://arxiv.org/abs/2505.09274v1
• Date: Wed, 14 May 2025 10:48:37 GMT
• Title: Recent Advances in Medical Imaging Segmentation: A Survey
• Authors: Fares Bougourzi, Abdenour Hadid,
• Abstract summary: Generative AI, Few-Shot Learning, Foundation Models, and Universal Models offered promising solutions to longstanding challenges.<n>We discuss inherent limitations, unresolved issues, and future research directions aimed at enhancing the practicality and accessibility of segmentation models in medical imaging.
• Score: 7.72661433458686
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Medical imaging is a cornerstone of modern healthcare, driving advancements in diagnosis, treatment planning, and patient care. Among its various tasks, segmentation remains one of the most challenging problem due to factors such as data accessibility, annotation complexity, structural variability, variation in medical imaging modalities, and privacy constraints. Despite recent progress, achieving robust generalization and domain adaptation remains a significant hurdle, particularly given the resource-intensive nature of some proposed models and their reliance on domain expertise. This survey explores cutting-edge advancements in medical image segmentation, focusing on methodologies such as Generative AI, Few-Shot Learning, Foundation Models, and Universal Models. These approaches offer promising solutions to longstanding challenges. We provide a comprehensive overview of the theoretical foundations, state-of-the-art techniques, and recent applications of these methods. Finally, we discuss inherent limitations, unresolved issues, and future research directions aimed at enhancing the practicality and accessibility of segmentation models in medical imaging. We are maintaining a \href{https://github.com/faresbougourzi/Awesome-DL-for-Medical-Imaging-Segmentation}{GitHub Repository} to continue tracking and updating innovations in this field.

Related papers

• Multimodal Causal-Driven Representation Learning for Generalizable Medical Image Segmentation [56.52520416420957]
  We propose Multimodal Causal-Driven Representation Learning (MCDRL) to tackle domain generalization in medical image segmentation.<n>MCDRL consistently outperforms competing methods, yielding superior segmentation accuracy and exhibiting robust generalizability.
  arXiv  Detail & Related papers  (2025-08-07T03:41:41Z)
• A systematic review of challenges and proposed solutions in modeling multimodal data [0.9674145073701153]
  Multimodal data modeling has emerged as a powerful approach in clinical research.<n>This systematic review synthesizes findings from 69 studies to identify common obstacles.<n>We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions.
  arXiv  Detail & Related papers  (2025-05-11T11:23:51Z)
• Vision Foundation Models in Medical Image Analysis: Advances and Challenges [7.224426395050136]
  Vision Foundation Models (VFMs) have sparked significant advances in the field of medical image analysis.<n>This paper reviews the state-of-the-art research on the adaptation of VFMs to medical image segmentation.<n>We discuss the latest developments in adapter-based improvements, knowledge distillation techniques, and multi-scale contextual feature modeling.
  arXiv  Detail & Related papers  (2025-02-20T14:13:46Z)
• Efficient MedSAMs: Segment Anything in Medical Images on Laptop [69.28565867103542]
  We organized the first international competition dedicated to promptable medical image segmentation.<n>The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline.<n>The best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption.
  arXiv  Detail & Related papers  (2024-12-20T17:33:35Z)
• Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models [7.987836953849249]
  Existing segmentation algorithms mostly rely on the availability of fully annotated images with pixel-wise annotations for training. To alleviate this challenge, there has been a growing focus on developing segmentation methods that can train deep models with weak annotations. The emergence of vision foundation models, notably the Segment Anything Model (SAM), has introduced innovative capabilities for segmentation tasks using weak annotations.
  arXiv  Detail & Related papers  (2024-04-20T02:40:49Z)
• Artificial General Intelligence for Medical Imaging Analysis [92.3940918983821]
  Large-scale Artificial General Intelligence (AGI) models have achieved unprecedented success in a variety of general domain tasks. These models face notable challenges arising from the medical field's inherent complexities and unique characteristics. This review aims to offer insights into the future implications of AGI in medical imaging, healthcare, and beyond.
  arXiv  Detail & Related papers  (2023-06-08T18:04:13Z)
• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review [0.5893124686141781]
  Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow. With the advent of modern hardware, the recent availability of large-scale quality public datasets and organized grand challenges have seen a surge in automated methods. In this survey, 126 papers illustrating the AI based methods for nuclei and glands segmentation published in the last five years-2022) are deeply analyzed.
  arXiv  Detail & Related papers  (2022-08-26T06:52:15Z)
• Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation [8.946871799178338]
  We present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation. We analyze and discuss the limitations and several unsolved problems of existing approaches.
  arXiv  Detail & Related papers  (2022-07-28T15:57:46Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.