Medical Image Segmentation Using Deep Learning: A Survey

By Tao Lei, Risheng Wang, Yong Wan, Xiaogang Du, Hongying Meng, Asoke Kumar Nandi. IET Image Processing, 2020

865 citations recorded by the source corpus.

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/320f80bf1458e4312724d06557138e093ab4497c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wuyang Li
2. Dong Liu
3. Yixuan Yuan
4. Xinyu Liu
5. Chenxin Li
6. Hengyu Liu
7. Cheng Wang
8. Kaiwen Zhang

In Computer Vision and Pattern Recognition

1. Wuyang Li
2. Dong Liu
3. Yixuan Yuan
4. Xinyu Liu
5. Chenxin Li
6. Hengyu Liu
7. Cheng Wang
8. Kaiwen Zhang

Citations in the corpus

• Customized Segment Anything Model for Medical Image Segmentation (2023, background): “Medical image segmentation aims at indicating the anatomical or pathological structure of the corresponding tissues as required to facilitate computer-aided diagnosis and intelligent clinical surgery [20].”
• Image Segmentation Using Deep Learning: A Survey (2020, neutral): “This survey, a revised version of [14], covers the recent literature in deep-learning-based image segmentation, including more than 100 such segmentation methods proposed to”
• U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation (2024, background): “Among these, U-Net [71] is a landmark work that initially demonstrated the effectiveness of encoder-decoder convolutional networks with skip connections for medical image segmentation [13, 43, 84, 90].”