Journal of Bionic Engineering ›› 2024, Vol. 21 ›› Issue (4): 2073-2085.doi: 10.1007/s42235-024-00499-2
Dendritic Learning and Miss Region Detection‑Based Deep Network for Multi‑scale Medical Segmentation
Lin Zhong1 ; Zhipeng Liu1 ; Houtian He1 ; Zhenyu Lei1 ; Shangce Gao1
- 1 Faculty of Engineering, University of Toyama, Toyama 9300887, Japan
Dendritic Learning and Miss Region Detection‑Based Deep Network for Multi‑scale Medical Segmentation
Lin Zhong1 ; Zhipeng Liu1 ; Houtian He1 ; Zhenyu Lei1 ; Shangce Gao1
- 1 Faculty of Engineering, University of Toyama, Toyama 9300887, Japan
摘要： Automatic identifcation and segmentation of lesions in medical images has become a focus area for researchers. Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specifc tissues, organs, or lesions from complex medical images, which is crucial for early diagnosis of diseases, treatment planning, and efcacy tracking. This paper introduces a deep network based on dendritic learning and missing region detection (DMNet), a new approach to medical image segmentation. DMNet combines a dendritic neuron model (DNM) with an improved SegNet framework to improve segmentation accuracy, especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis. This work provides a new approach to medical image segmentation and confrms its efectiveness. Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics, proving its efectiveness and stability in medical image segmentation tasks.