Segmentation of lung cancer-caused metastatic lesions in bone scan images using self-defined model with deep supervision

To automatically identify and delineate metastatic lesions in low-resolution bone scan images, we propose a deep learning-based segmentation method in this paper. In particular, the view aggregation in this method uses a pixel-wise addition to enhance the regions with high uptake of the radiopharmaceutical. The operation of view aggregation augments images for the lesion segmentation task. By following the structure of the encoder-decoder with deep supervision, our model is an end-to-end segmentation network that consists of two sub-networks of feature extraction and pixel classification. As such, the hieratical features of bone scan images can be learned by the feature extraction sub-network. The pixels in metastasis areas within a feature map are then identified and delineated by the pixel classification sub-network. The results of experiments on clinical bone scan images show that the proposed model performs well in segmenting metastatic lesions automatically, obtaining a mean score of 0.6556 on DSC (Dice Similarity Coefficient). However, more bone scan images enable our model to learn better representative features of metastatic lesions, for further improving the performance of deep learning-based lesion segmentation.

Automated summary

In this paper, we propose a deep learning-based segmentation method to automatically identify and delineate metastatic lesions in low-resolution bone scan images. The method utilizes view aggregation and feature extraction to achieve automatic lesion segmentation, and performs well in clinical experiments.