Mass detection in automated 3-D breast ultrasound using a patch Bi-ConvLSTM network

Breast cancer mortality can be significantly reduced by early detection of its symptoms. The 3-D Automated Breast Ultrasound (ABUS) has been widely used for breast screening due to its high sensitivity and reproducibility. The large number of ABUS slices, and high variation in size and shape of the masses, make the manual evaluation a challenging and time-consuming process. To assist the radiologists, we propose a convolutional BiLSTM network to classify the slices based on the presence of a mass. Because of its patch-based architecture, this model produces the approximate location of masses as a heat map. The prepared dataset consists of 60 volumes belonging to 43 patients. The precision, recall, accuracy, F1-score, and AUC of the proposed model for slice classification were 84%, 84%, 93%, 84%, and 97%, respectively. Based on the FROC analysis, the proposed detector obtained a sensitivity of 82% with two false positives per volume.

Automated summary

Early detection of breast cancer symptoms is crucial in reducing mortality rates. The 3-D Automated Breast Ultrasound (ABUS) is popular for breast screening due to its sensitivity and reproducibility. However, manual evaluation of ABUS slices is challenging and time-consuming. To aid radiologists, a convolutional BiLSTM network was proposed for slice classification, which also identifies the approximate location of masses as a heat map. Results showed an 84% precision, 84% recall, 93% accuracy, 84% F1-score, and 97% AUC for the proposed model. FROC analysis revealed a sensitivity of 82% with two false positives per volume.