Structural uncertainty estimation for medical image segmentation

• Published in: Medical image analysis Vol. 103; p. 103602
• Main Authors: Yang, Bing, Zhang, Xiaoqing, Zhang, Huihong, Li, Sanqian, Higashita, Risa, Liu, Jiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2025
• Subjects: Algorithms; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Medical image segmentation; Neural Networks, Computer; Reproducibility of Results; Structural uncertainty; Ultrasonography; Uncertainty; Uncertainty estimation; Structural uncertainty; Medical image segmentation; Uncertainty estimation
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2025.103602

Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to erroneous uncertainty indications, and (2) bring attention interference, resulting in the waste of extensive details and potential understanding confusion. In this paper, we propose a novel structural uncertainty estimation method, based on Convolutional Neural Networks (CNN) and Active Shape Models (ASM), named SU-ASM, which incorporates global shape information for providing precise segmentation and uncertainty estimation. The SU-ASM consists of three components. Firstly, multi-task generation provides multiple outcomes to assist ASM initialization and shape optimization via a multi-task learning module. Secondly, information fusion involves the creation of a Combined Boundary Probability (CBP) and along with a rapid shape initialization algorithm, Key Landmark Template Matching (KLTM), to enhance boundary reliability and select appropriate shape templates. Finally, shape model fitting where multiple shape templates are matched to the CBP while maintaining their intrinsic shape characteristics. Fitted shapes generate segmentation results and structural uncertainty estimations. The SU-ASM has been validated on cardiac ultrasound dataset, ciliary muscle dataset of the anterior eye segment, and the chest X-ray dataset. It outperforms state-of-the-art methods in terms of segmentation and uncertainty estimation. •We propose an uncertainty estimation method via ASM that also improves segmentation.•We integrate CNN and ASM for high-confidence boundaries and rapid shape initialization.•We introduce an objective function preserving shape styles while fitting boundary potential.