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.

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1095325 (15 March 2019); doi: 10.1117/12.2514000

Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)