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[Medical image instance segmentation: from candidate region to no candidate region]
Tao Zhou1,2, Yanan Zhao1, Huiling Lu3
1School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, P. R. China.
Summary
This review summarizes medical image instance segmentation, detailing its principles, models, and applications in areas like CT scans and X-rays. It highlights challenges and future directions for this key image processing technique.
Area of Science:
- Computer Vision
- Medical Imaging Analysis
Context:
- Instance segmentation is crucial for precise object delineation in medical images.
- It assigns instance-level labels, differentiating objects of the same class.
Purpose:
- To systematically review instance segmentation principles and models in medical imaging.
- To explore the application landscape and future trends of medical image instance segmentation.
Summary:
- Classifies instance segmentation models into two-stage, single-stage, and 3D approaches.
- Details applications in diverse medical images including colon, cervical, bone, gastric, lung CT, and breast X-ray.
- Discusses current challenges and future research directions in the field.
Impact:
- Provides a comprehensive overview for researchers and practitioners in medical image analysis.
- Offers guidance for developing advanced instance segmentation techniques for improved diagnostics.
