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Christ, P.F., Elshaer, M.E., Ettlinger, F., et al. (2016) Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields. International Conference on Medical Image Computing and Computer Assisted Intervention, Athens, 17-21 October 2016, 415-423.
https://doi.org/10.1007/978-3-319-46723-8_48

has been cited by the following article:

• 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.