首页|Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation

Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation

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Automatic identification and segmentation of lesions in medical images has become a focus area for researchers.Segmenta-tion for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues,organs,or lesions from complex medical images,which is crucial for early diagnosis of diseases,treatment planning,and efficacy tracking.This paper introduces a deep network based on dendritic learning and missing region detec-tion(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 confirms its effective-ness.Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics,proving its effectiveness and stability in medical image segmentation tasks.

Medical image segmentationDendritic learningDeep supervisionDynamic focal loss

Lin Zhong、Zhipeng Liu、Houtian He、Zhenyu Lei、Shangce Gao

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Faculty of Engineering,University of Toyama,Toyama 9300887,Japan

日本学术振兴会项目Japan Science and Technology Agency(JST)Support for Pioneering Research Initiated by the Next Generation(SPRING)JST through the Establishment of University Fellowships Towards the Creation of Science Technology Innovation

JP22H03643JPMJSP2145JPMJFS2115

2024

10.1007/s42235-024-00499-2

仿生工程学报(英文版)
吉林大学

仿生工程学报(英文版)

CSTPCDEI
影响因子：0.837
ISSN：1672-6529
年,卷(期)：2024.21(4)