Segmenting microcalcifications in mammograms and its applications

Roee Zamir; Shai Bagon; David Samocha; Yael Yagil; Ronen Basri; Miri Sklair-Levy; Meirav Galun

doi:10.1117/12.2580398

15 February 2021 Segmenting microcalcifications in mammograms and its applications

Roee Zamir, Shai Bagon, David Samocha, Yael Yagil, Ronen Basri, Miri Sklair-Levy, Meirav Galun

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962W (2021) https://doi.org/10.1117/12.2580398
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Microcalcifications are small deposits of calcium that appear in mammograms as bright white specks on the soft tissue background of the breast. Microcalcifications may be a unique indication for Ductal Carcinoma in Situ breast cancer, and therefore their accurate detection is crucial for diagnosis and screening. Manual detection of these tiny calcium residues in mammograms is both time-consuming and error-prone, even for expert radiologists, since these microcalcifications are small and can be easily missed. Existing computerized algorithms for detecting and segmenting microcalcifications tend to suffer from a high false-positive rate, hindering their widespread use. In this paper, we propose an accurate calcification segmentation method using deep learning. We specifically address the challenge of keeping the false positive rate low by suggesting a strategy for focusing the hard pixels in the training phase. Furthermore, our accurate segmentation enables extracting meaningful statistics on clusters of microcalcifications.

Conference Presentation

Citation Download Citation

Roee Zamir, Shai Bagon, David Samocha, Yael Yagil, Ronen Basri, Miri Sklair-Levy, and Meirav Galun "Segmenting microcalcifications in mammograms and its applications", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962W (15 February 2021); https://doi.org/10.1117/12.2580398

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