Validation report on Segasist P-AC published in the Red Journal

Title: A Multiphase Validation of Atlas-Based Automatic and Semiautomatic Segmentation Strategies for Prostate MRI Authors: Spencer Martin, BSc, George Rodrigues, MD, MSc, Nikhilesh Patil, MD, Glenn Bauman, MD, • David D'Souza, MD, Tracy Sexton, MD, David Palma, MD, Alexander V. Louie, MD, Farzad Khalvati, PhD, Hamid R. Tizhoosh, PhD, Stewart Gaede, PhD Received 25 July 2011; received in revised form 9 February 2012; accepted 13 March 2012. published online 09 May 2012. International Journal of Radiation Oncology * Biology * Physics

Abstract

Purpose

To perform a rigorous technological assessment and statistical validation of a software technology for anatomic delineations of the prostate on MRI datasets.

Methods and Materials

A 3-phase validation strategy was used. Phase I consisted of anatomic atlas building using 100 prostate cancer MRI data sets to provide training data sets for the segmentation algorithms. In phase II, 2 experts contoured 15 new MRI prostate cancer cases using 3 approaches (manual, N points, and region of interest). In phase III, 5 new physicians with variable MRI prostate contouring experience segmented the same 15 phase II datasets using 3 approaches: manual, N points with no editing, and full autosegmentation with user editing allowed. Statistical analyses for time and accuracy (using Dice similarity coefficient) endpoints used traditional descriptive statistics, analysis of variance, analysis of covariance, and pooled Student t test.

Results

In phase I, average (SD) total and per slice contouring time for the 2 physicians was 228 (75), 17 (3.5), 209 (65), and 15 seconds (3.9), respectively. In phase II, statistically significant differences in physician contouring time were observed based on physician, type of contouring, and case sequence. The N points strategy resulted in superior segmentation accuracy when initial autosegmented contours were compared with final contours. In phase III, statistically significant differences in contouring time were observed based on physician, type of contouring, and case sequence again. The average relative timesaving for N points and autosegmentation were 49% and 27%, respectively, compared with manual contouring. The N points and autosegmentation strategies resulted in average Dice values of 0.89 and 0.88, respectively. Pre- and postedited autosegmented contours demonstrated a higher average Dice similarity coefficient of 0.94.

Conclusion

The software provided robust contours with minimal editing required. Observed time savings were seen for all physicians irrespective of experience level and baseline manual contouring speed.