Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index

Yifan Peng; Rui Hou; Yinhao Ren; Lars J. Grimm; Jeffrey R. Marks; E. Shelley Hwang; Joseph Y. Lo

doi:10.1117/12.2551263

16 March 2020 Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index

Yifan Peng, Rui Hou, Yinhao Ren, Lars J. Grimm, Jeffrey R. Marks, E. Shelley Hwang, Joseph Y. Lo

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 1131403 (2020) https://doi.org/10.1117/12.2551263
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Detecting microcalcification clusters in mammograms is important to the diagnosis of breast diseases. Previous studies which mainly focused on supervised methods require abundant annotated training data but these data are usually hard to acquire. In this work, we leverage unsupervised convolutional autoencoders and structural similarity (SSIM) based post-processing to detect and localize microcalcification clusters in full-field digital mammograms (FFDMs). Our models were trained by patches extracted from 3,632 normal cases, in total with 16,702 mammograms. Evaluations were conducted in three aspects, including patch-based anomaly detection, pixel-wise microcalcification localization, and microcalcification cluster detection. Specifically, the receiver operating characteristic (ROC) analysis was used for patch-based anomaly detection. Then, a pixel-wise ROC analysis and a cluster-based free-response ROC (FROC) analysis were performed to assess our detection algorithms of individual microcalcifications and microcalcification clusters, respectively. We achieved a pixel-wise AUC of 0.97 as well as a cluster-based sensitivity of 0.62 at 1 false positive per image and 0.75 at 2.5 false positives per image. Both qualitative and quantitative results demonstrated the effectiveness of our method.

Citation Download Citation

Yifan Peng, Rui Hou, Yinhao Ren, Lars J. Grimm, Jeffrey R. Marks, E. Shelley Hwang, and Joseph Y. Lo "Microcalcification localization and cluster detection using unsupervised convolutional autoencoders and structural similarity index", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 1131403 (16 March 2020); https://doi.org/10.1117/12.2551263

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