Cascaded convolutional neural networks for spine chordoma tumor segmentation from MRI

Syed M. S. Reza; Snehashis Roy; Deric M. Park; Dzung L. Pham; John A. Butman

doi:10.1117/12.2514000

15 March 2019 Cascaded convolutional neural networks for spine chordoma tumor segmentation from MRI

Syed M. S. Reza, Snehashis Roy, Deric M. Park, Dzung L. Pham, John A. Butman

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1095325 (2019) https://doi.org/10.1117/12.2514000
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Chordoma is a rare type of tumor that usually appears in the bone near the spinal cord and skull base. Due to their location in the skull base and diverse appearance in size and shape, automatic segmentation of chordoma tumors from magnetic resonance images (MRI) is a challenging task. In addition, similar MR intensity distributions of different anatomical regions, specifically sinuses, make the segmentation task from MRI more challenging. In comparison, most of the state-of-the-art lesion segmentation methods are designed to segment pathologies inside the brain. In this work, we propose an automatic chordoma segmentation framework using two cascaded 3D convolutional neural networks (CNN) via an auto-context model. While the first network learns to detect all potential tumor voxels, the second network fine-tunes the classifier to distinguish true tumor voxels from the false positives detected by the first network. The proposed method is evaluated using multi-contrast MR images of 22 longitudinal scans from 8 patients. Preliminary results showed a linear correlation of 0.71 between the detected and manually outlined tumor volumes, compared to 0.40 for a random forest (RF) based method. Furthermore, the response of tumor growth over time, i.e. increasing, decreasing, or stable, is evaluated according to the response evaluation criteria in solid tumors with an outcome of 0.26 kappa coefficient, compared to 0.13 for the RF based method.

Citation Download Citation

Syed M. S. Reza, Snehashis Roy, Deric M. Park, Dzung L. Pham, and John A. Butman "Cascaded convolutional neural networks for spine chordoma tumor segmentation from MRI", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1095325 (15 March 2019); https://doi.org/10.1117/12.2514000

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