Classification of thoracic bone scintigraphic images using ResNet with attention modules

Xu Cao; Yongchun Cao; Qiang Lin; Zhengxing Man; Yubo Wang

doi:10.1117/12.2643937

9 October 2022 Classification of thoracic bone scintigraphic images using ResNet with attention modules

Xu Cao, Yongchun Cao, Qiang Lin, Zhengxing Man, Yubo Wang

Proceedings Volume 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022); 122460Z (2022) https://doi.org/10.1117/12.2643937
Event: 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 2022, Qingdao, China

Abstract

Whole-body bone scan is an effective tool for diagnosing bone metastasis from malignant tumors, including lung cancer. However, due to the low spatial resolution of Scintigraphic images, manual analysis of the images is very difficult. In order to accurately diagnose bone metastases, a self-defined convolutional neural network is studied and constructed in this paper for detecting lung cancer-Caused skeletal metastasis in bone scan images Firstly, since thoracic regions such as ribs and spine are the most frequent areas of bone metastases, we preprocessed the collected SPECT whole-body bone imaging data to extract the thoracic regions. Then based on the feature that ResNet can alleviate the vanishing gradient problem caused by increasing depth in deep neural networks and the feature that attention can focus on important information to improve network expression. A deep SPECT image classification network Att-ResNet24 with residual module and hybrid attention mechanism is developed for classification of these images, including bone metastases and non-bone metastases. Experiments on a set of real SPETC whole-body bone images show that self-defined classification networks can be effectively used for the detection of bone metastases, obtaining the best scores 0.737, 0.744, 0.736, and 0.735 for accuracy, precision, recall, and F-1 score, respectively.

Citation Download Citation

Xu Cao, Yongchun Cao, Qiang Lin, Zhengxing Man, and Yubo Wang "Classification of thoracic bone scintigraphic images using ResNet with attention modules", Proc. SPIE 12246, 2nd International Conference on Signal Image Processing and Communication (ICSIPC 2022), 122460Z (9 October 2022); https://doi.org/10.1117/12.2643937

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