dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis

BMC Medical Imaging

Haijun Wang¹  Qiang Lin²  Tongtong Li²  Chuangui Cao²  Zhengxing Man²  Yongchun Cao² 

[1] Department of Nuclear Medicine, Gansu Provincial Hospital, 730000, Lanzhou, China;School of Mathematics and Computer Science, Northwest Minzu University, 730030, Lanzhou, China;Key Lab of Streaming Data Computing and Applications, Northwest Minzu University, 730030, Lanzhou, China;

关键词: SPECT bone scintigraphy;    Automated diagnosis;    Image classification;    Deep learning;    CNN;   

DOI  :  10.1186/s12880-021-00653-w

来源: Springer

【 摘 要 】

BackgroundFunctional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly characterized by poor resolution, low signal-to-noise ratio, as well as the high sensitivity and low specificity because of the visually similar characteristics of lesions between diseases on imaging findings.MethodsFocusing on the automated diagnosis of diseases with whole-body SPECT scintigraphic images, in this work, a self-defined convolutional neural network is developed to survey the presence or absence of diseases of concern. The data preprocessing mainly including data augmentation is first conducted to cope with the problem of limited samples of SPECT images by applying the geometric transformation operations and generative adversarial network techniques on the original SPECT imaging data. An end-to-end deep SPECT image classification network named dSPIC is developed to extract the optimal features from images and then to classify these images into classes, including metastasis, arthritis, and normal, where there may be multiple diseases existing in a single image.ResultsA group of real-world data of whole-body SPECT images is used to evaluate the self-defined network, obtaining a best (worst) value of 0.7747 (0.6910), 0.7883 (0.7407), 0.7863 (0.6956), 0.8820 (0.8273) and 0.7860 (0.7230) for accuracy, precision, sensitivity, specificity, and F-1 score, respectively, on the testing samples from the original and augmented datasets.ConclusionsThe prominent classification performance in contrast to other related deep classifiers including the classical AlexNet network demonstrates that the built deep network dSPIC is workable and promising for the multi-disease, multi-lesion classification task of whole-body SPECT bone scintigraphy images.

【 授权许可】

CC BY