Automated Segmentation of Suspicious Breast Masses from Ultrasound Images Viksit Kumar, Jeremy Webb, Adriana Gregory, Mostafa Fatemi, Azra Alizad GTC 2018, San Jose, USA
SIGNIFICANCE Breast cancer is most common and leading cause of death in American women* Segmentation and classification of suspicious breast masses can avoid unnecessary biopsies Conventional segmentation algorithm requires an initial seed Core Needle biopsy for suspicious breast Sørensen – Dice performance of 0.82-0.86, a masses measure of similarity of segmentation *Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA: a cancer journal for clinicians. 2015 2 *Shulman LN, Willett W, Sievers A, Knaul FM. Breast Cancer in Developing Countries: Opportunities for Improved Survival. Journal of Oncology. 2010;2010. doi: 10.1155/2010/595167
GOALS • Develop an automated real-time detection and segmentation of suspicious breast masses • Currently ultrasonographers use the morphological and textural features to identify suspicious masses based on suggestions from American College of Radiology • The suspicious masses are then scored based on the Breast Imaging Reporting and Data System (BI-RADS) scale and is the basis for recommending core needle biopsy • Automated detection process can aid in rapid localization of suspicious masses • Automated segmentation and classification process can assist in assigning BI-RADS score and recommending biopsy
ULTRASOUND IMAGING Beam In-phase Raw channel Transducer formed RF quadrature RF data data data Post processed B-mode B-mode image image 4
MATERIALS AND METHODS • 258 patients, 433 images from multiple cross-sections, 148 cases of BI-RADS 4 • 124 malignant and 134 benign cases • Equal number of images from General Electric LOGIQ E9 and Philips IU22 • Algorithm: Multi U-net with majority voting • Loss function: negative dice coefficient • Testing in real-time on Titan xp (13.83 ms/image)
ARCHITECTURE 6 Ronneberger O, Fischer P, Brox T, editors. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention; 2015: Springer
SEGMENTATION RESULTS-CONTD. Red outline = Expert segmentation Blue outline = Predicted segmentation 2(b) Dice coefficient = 0.94 B-mode image of a benign-cellular fibro epithelial mass. The mass has typical smooth boundaries of a benign mass and is oval in shape
SEGMENTATION RESULTS-CONTD. Red outline = Expert segmentation Blue outline = Predicted segmentation 3(a) 3(b) Dice coefficient = 0.88 B-mode image for benign fat necrosis with dystrophic calcifications. Notice the posterior acoustic shadowing beneath the benign mass
SEGMENTATION RESULTS-CONTD. Red outline = Expert segmentation Blue outline = Predicted segmentation 4(a) 4(b) Dice coefficient = 0.88 The biopsy result from the suspicious mass was malignant invasive/infiltrating ductal carcinoma, grade III. The irregular boundaries of the mass are typical of malignant masses which are usually challenging cases for segmentation algorithms
RESULTS: CINE CLIP 1 Benign fibroadenoma
CONTD. CINE CLIP 2 Invasive Ductal carcinoma G.II/III
CONCLUSION • Mean Sørensen – Dice coefficient of 0.82 • Real-time application with no need for initial seed • Can be used for segmentation and detection of suspicious breast masses • Segmentation masks can be used for classification of suspicious masses • Reduce localization time and aid sonographer in categorizing the suspicious mass • Applications in point of care, mobile health monitoring, assisting sonographers • Provides the expertise of an expert sonographer to sonographers in training
ACKNOWLEDGEMENT • We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research • We gratefully acknowledge the support of Amazon web services for the donation of credits used for this research • This work was supported by National Institute of health grants R01CA148994 from the National Cancer Institute 13
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