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2018 GTC San Jose

S8525 - Automated Segmentation of Suspicious Breast Masses from Ultrasound Images

Session Speakers
Session Description

Learn how to apply deep learning for detecting and segmenting suspicious breast masses from ultrasound images. Ultrasound images are challenging to work with due to the lack of standardization of image formation. Learn the appropriate data augmentation techniques, which do not violate the physics of ultrasound imaging. Explore the possibilities of using raw ultrasound data to increase performance. Ultrasound images collected from two different commercial machines are used to train an algorithm to segment suspicious breast with a mean dice coefficient of 0.82. The algorithm is shown to perform at par with conventional seeded algorithm. However, a drastic reduction in computation time is observed enabling real-time segmentation and detection of breast masses.


Additional Information
Medical Imaging and Radiology
Healthcare & Life Sciences
Intermediate technical
Talk
25 minutes
Session Schedule