Measuring Efficiency of Semi-automated Brain Tumor Segmentation by Simulating User Interaction

• Published in: Frontiers in computational neuroscience 2020-04, Vol.14, p.32-32
• Main Authors: Gering, David, Kotrotsou, Aikaterini, Young-Moxon, Brett, Miller, Neal, Avery, Aaron, Kohli, Lisa, Knapp, Haley, Hoffman, Jeffrey, Chylla, Roger, Peitzman, Linda, Mackie, Thomas R
• Format: Article
• Language: English
• Subjects: Accuracy; Automation; brain MRI; Brain research; Brain tumors; Computer simulation; deep learning; efficiency; Experiments; Feedback; glioma; Image processing; Neuroscience; Segmentation; tumor; Tumors
• ISSN: 1662-5188; 1662-5188
• DOI: 10.3389/fncom.2020.00032

Traditionally, radiologists have crudely quantified tumor extent by measuring the longest and shortest dimension by dragging a cursor between opposite boundary points across a single image rather than full segmentation of the volumetric extent. For algorithmic-based volumetric segmentation, the degree of radiologist experiential involvement varies from confirming a fully automated segmentation, to making a single drag on an image to initiate semi-automated segmentation, to making multiple drags and clicks on multiple images during interactive segmentation. An experiment was designed to test an algorithm that allows various levels of interaction. Given the ground-truth of the BraTS training data, which delimits the brain tumors of 285 patients on multi-spectral MR, a computer simulation mimicked the process that a radiologist would follow to perform segmentation with real-time interaction. Clicks and drags were placed only where needed in response to the deviation between real-time segmentation results and assumed radiologist's goal, as provided by the ground-truth. Results of accuracy for various levels of interaction are presented along with estimated elapsed time, in order to measure efficiency. Average total elapsed time, including loading the study through confirming 3D contours, was 46 s.