TITLE:
U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams
AUTHORS:
Yang He, Qiang Lin, Yongchun Cao, Zhengxing Man
KEYWORDS:
Tumor Bone Metastasis, Bone Scintigram, Lesion Segmentation, CNN, Dual Pooling
JOURNAL NAME:
Journal of Computer and Communications,
Vol.12 No.4,
April
15,
2024
ABSTRACT: In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.