0 citations0 references

Exploring the Effectiveness of Multi-Objective Training for Organ Substructure Segmentation in Medical Imaging

Studies in health technology and informatics2023Vol. 308, pp. 146–154

Citations Over TimeTop 10% of 2023 papers

Wenxin Duan, Yingwei Guo, Yingjian Yang, Nanrong Zeng, Shicong Wang, Jiaxuan Xu, Rongchang Chen, Yan Kang

Abstract

Chronic obstructive pulmonary disease (COPD) is closely related to the right ventricle and lung lobes. This study focuses on the segmentation of the right ventricle and lung lobes. We conducted experiments using the MMWHS and our lung lobe datasets and evaluated the segmentation using different training models. We observed that the multi-objective segmentation approach has advantages over single-objective segmentation in segmenting the right ventricle and lung lobes. For the segmentation of the right ventricle, the multi-objective segmentation approach yielded an improvement of 2.0% in the Dice coefficient and 2.5% in the Jaccard index compared to single-objective segmentation. For the segmentation of five lung lobes, the multi-objective segmentation outperformed the single-objective segmentation with Dice coefficient improvements of 1.4%, 1.0%, 1.5%, 0.7%, and 1.3%, respectively.

Related Papers

→ Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review(2019)64 cited
→ Adaptive t-vMF dice loss: An effective expansion of dice loss for medical image segmentation(2023)46 cited
SOME ROUGH NEUTROSOPHIC SIMILARITY MEASURES AND THEIR APPLICATION TO MULTI ATTRIBUTE DECISION MAKING(2015)
→ Medical Image Segmentation with Stochastic Aggregated Loss in a Unified U-Net(2019)11 cited
→ [Automatic segmentation of head and neck organs at risk based on three-dimensional U-NET deep convolutional neural network].(2020)4 cited