Medical image instance segmentation: from candidate region to no candidate region.
10.7507/1001-5515.202201034
- Author:
Tao ZHOU
1
;
Yanan ZHAO
1
;
Huiling LU
2
;
Senbao HOU
1
;
Xiaomin ZHENG
3
Author Information
1. School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, P. R. China.
2. School of Science, Ningxia Medical University, Yinchuan 750004, P. R. China.
3. Research Institute for Reproductive Medicine and Genetic Diseases, Wuxi Maternity and Child Health Hospital, Wuxi, Jiangsu 214002, P. R. China.
- Publication Type:Journal Article
- Keywords:
Candidate region;
Medical image instance segmentation;
No candidate region;
Single-stage instance segmentation;
Two-stage instance segmentation
- MeSH:
Imaging, Three-Dimensional/methods*;
Image Processing, Computer-Assisted;
Tomography, X-Ray Computed/methods*;
Algorithms
- From:
Journal of Biomedical Engineering
2022;39(6):1218-1232
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, the task of object detection and segmentation in medical image is the research hotspot and difficulty in the field of image processing. Instance segmentation provides instance-level labels for different objects belonging to the same class, so it is widely used in the field of medical image processing. In this paper, medical image instance segmentation was summarized from the following aspects: First, the basic principle of instance segmentation was described, the instance segmentation models were classified into three categories, the development context of the instance segmentation algorithm was displayed in two-dimensional space, and six classic model diagrams of instance segmentation were given. Second, from the perspective of the three models of two-stage instance segmentation, single-stage instance segmentation and three-dimensional (3D) instance segmentation, we summarized the ideas of the three types of models, discussed the advantages and disadvantages, and sorted out the latest developments. Third, the application status of instance segmentation in six medical images such as colon tissue image, cervical image, bone imaging image, pathological section image of gastric cancer, computed tomography (CT) image of lung nodule and X-ray image of breast was summarized. Fourth, the main challenges in the field of medical image instance segmentation were discussed and the future development direction was prospected. In this paper, the principle, models and characteristics of instance segmentation are systematically summarized, as well as the application of instance segmentation in the field of medical image processing, which is of positive guiding significance to the study of instance segmentation.
