EE Seminar: Towards Automatic Lesion Detection in 3D Prostate MRI Scans

Speaker: Ophir Yaniv

M.Sc. student under the supervision of Prof. Nahum Kiryati and Dr. Arnaldo Mayer

Sunday, January 26^th, 2020 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Towards Automatic Lesion Detection in 3D Prostate MRI Scans

Abstract

Prostate cancer is the second most common cancer among men. Early detection is critical to the success of its treatment. Thanks to the excellent contrast it provides in soft tissues, prostate MRI has become the tool of choice in prostate cancer imaging.

We present a novel approach for the automated detection of prostate lesions in 3D MRI scans. Our methodology divides the workflow into two stages: 1) Segmentation of the prostate within the MRI scan to remove unrelated tissues. 2) Detection of suspected malignancies within the segmented prostate.

Previous methods of 3D prostate segmentation required high computational power and memory which are usually not available on the PCs used by radiologists. We propose a novel 3D deep neural network architecture, called V-net Light (VnL), that is based on a computationally efficient 3D Module, called 3D Light. The resulting network minimizes the number of parameters while maintaining state-of-the-art segmentation results.

Qualitative and quantitative validation of the proposed architecture will be presented.

To conclude, several directions for the lesion detection stage, to be developed in continuation of this research, will be discussed.