KTNet: towards automated 2D kidney and tumor segmentation

Rahul Rajendran; Shreyas Kamath K. M.; Karen Panetta; Sos Agaian

doi:10.1117/12.2619006

27 May 2022 KTNet: towards automated 2D kidney and tumor segmentation

Rahul Rajendran, Shreyas Kamath K. M., Karen Panetta, Sos Agaian

Proceedings Volume 12100, Multimodal Image Exploitation and Learning 2022; 121000O (2022) https://doi.org/10.1117/12.2619006
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Conference Poster

Abstract

There are more than 400,000 new cases of kidney cancer each year, and surgery is its most common treatment. Accurate segmentation and characterization of kidneys and kidney tumors is an important step in quantifying the tumor's morphological details to monitor the progression of the disease and improve treatment planning. Segmentation of kidney tumors in CT images is a challenging task due to the low contrast, irregular motion, diverse shapes, and sizes. Furthermore, manual delineation techniques are extremely time-consuming and are prone to errors due to the variability between different specialists. The literature provides the application of 3D Convolutional Neural Networks (CNNs) for the segmentation of kidneys and tumors. While effective, 3D CNNs are computationally expensive. Our work proposes the applications of a novel 2D CNN architecture to segment kidneys and tumors from CT images. The proposed architecture uses features from enhanced images to improve the segmentation performance. Quantitative and qualitative analysis of the proposed model on the KiTS19 dataset shows the improvement against recent state-of-the-art architectures.

Citation Download Citation

Rahul Rajendran, Shreyas Kamath K. M., Karen Panetta, and Sos Agaian "KTNet: towards automated 2D kidney and tumor segmentation", Proc. SPIE 12100, Multimodal Image Exploitation and Learning 2022, 121000O (27 May 2022); https://doi.org/10.1117/12.2619006

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available