A multi-scale supervision and residual feedback optimization algorithm for improving optic chiasm and optic nerve segmentation accuracy in nasopharyngeal carcinoma CT images.
10.12122/j.issn.1673-4254.2025.03.21
- Author:
Jinyu LIU
1
;
Shujun LIANG
1
;
Yu ZHANG
1
Author Information
1. School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China.
- Publication Type:Journal Article
- Keywords:
deep supervision;
hybrid pooling strategy;
nasopharyngeal carcinoma;
optic chiasm and optic nerve segmentation;
residual feedback
- MeSH:
Humans;
Tomography, X-Ray Computed/methods*;
Optic Chiasm/diagnostic imaging*;
Optic Nerve/diagnostic imaging*;
Algorithms;
Nasopharyngeal Carcinoma;
Deep Learning;
Nasopharyngeal Neoplasms/diagnostic imaging*;
Neural Networks, Computer;
Image Processing, Computer-Assisted/methods*
- From:
Journal of Southern Medical University
2025;45(3):632-642
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:We propose a novel deep learning segmentation algorithm (DSRF) based on multi-scale supervision and residual feedback strategy for precise segmentation of the optic chiasm and optic nerves in CT images of nasopharyngeal carcinoma (NPC) patients.
METHODS:We collected 212 NPC CT images and their ground truth labels from SegRap2023, StructSeg2019 and HaN-Seg2023 datasets. Based on a hybrid pooling strategy, we designed a decoder (HPS) to reduce small organ feature loss during pooling in convolutional neural networks. This decoder uses adaptive and average pooling to refine high-level semantic features, which are integrated with primary semantic features to enable network learning of finer feature details. We employed multi-scale deep supervision layers to learn rich multi-scale and multi-level semantic features under deep supervision, thereby enhancing boundary identification of the optic chiasm and optic nerves. A residual feedback module that enables multiple iterations of the network was designed for contrast enhancement of the optic chiasm and optic nerves in CT images by utilizing information from fuzzy boundaries and easily confused regions to iteratively refine segmentation results under supervision. The entire segmentation framework was optimized with the loss from each iteration to enhance segmentation accuracy and boundary clarity. Ablation experiments and comparative experiments were conducted to evaluate the effectiveness of each component and the performance of the proposed model.
RESULTS:The DSRF algorithm could effectively enhance feature representation of small organs to achieve accurate segmentation of the optic chiasm and optic nerves with an average DSC of 0.837 and an ASSD of 0.351. Ablation experiments further verified the contributions of each component in the DSRF method.
CONCLUSIONS:The proposed deep learning segmentation algorithm can effectively enhance feature representation to achieve accurate segmentation of the optic chiasm and optic nerves in CT images of NPC.
