EE Seminar: Kidney Segmentation and Renal Lesion Detection in 3D CT
Speaker: Neta Gertzovsky
M.Sc. student under the supervision of Prof. Nahum Kiryati and Dr. Arnaldo Mayer
Wednesday, December 6th 2017 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering
Kidney Segmentation and Renal Lesion Detection in 3D CT
Abstract
Renal cysts are common in aging kidneys, and are usually found incidentally in patients undergoing abdominal imaging for other reasons. Although most cysts are benign, they require expert examination as some of them may either indicate the presence of a malignancy or evolve into one. So far, the proposed algorithms for the analysis and detection of renal cysts have been either semi-automatic or evaluated on fairly small data-sets. Here we present a fully automatic method to segment kidneys and to detect simple renal cysts. A fully convolutional neural network (FCN) is employed for segmentation of the kidneys. A combined 3D distance map of the kidneys and surrounding fluids provides initial candidates for cysts. Then, a convolutional neural network (CNN) classifies the candidates as cysts or non-cyst objects. Performance was evaluated on 52 randomly selected volumetric CT scans with 70 cysts annotated by an experienced radiologist, with promising results.
Another type of renal lesions are cancerous renal tumors. Though also usually an incidental finding, they are malignant and often fatal. Early detection of such tumors is highly advantageous for recovery. Using the same methods and similar data we attempted to develop a fully automatic system for detection of cancerous renal tumors. We present our experiments and preliminary results and discuss the steps we deem required to undertake this challenge.
