Author(s)

Yang He¹, Qiang Lin^2*, Yongchun Cao², Zhengxing Man²

¹Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China.
²School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China.

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.

Tumor Bone Metastasis, Bone Scintigram, Lesion Segmentation, CNN, Dual Pooling

He, Y. , Lin, Q. , Cao, Y. and Man, Z. (2024) U-Net Based Dual-Pooling Segmentation of Bone Metastases in Thoracic SPECT Bone Scintigrams. Journal of Computer and Communications, 12, 60-71. doi: 10.4236/jcc.2024.124006.