Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features.

Autosegmentation of prostate anatomy for radiation treatment planning using deep decision forests of radiomic features. Phys Med Biol. 2018 Nov 22;63(23):235002 Authors: Macomber MW, Phillips M, Tarapov I, Jena R, Nori A, Carter D, Folgoc LL, Criminisi A, Nyflot MJ Abstract Machine learning for image segmentation could provide expedited clinic workflow and better standardization of contour delineation. We evaluated a new model using deep decision forests of image features in order to contour pelvic anatomy on treatment planning CTs. 193 CT scans from one UK and two US institutions for patients undergoing radiotherapy treatment for prostate cancer from 2012-2016 were anonymized. A decision forest autosegmentation model was trained on a random selection of 94 images from Institution 1 and tested on 99 scans from Institution 1, 2, and 3. The accuracy of model contours was measured with the Dice similarity coefficient (DSC) and the median slice-wise Hausdorff distance (MSHD) using clinical contours as the ground truth reference. Two comparison studies were performed. The accuracy of the model was compared to four commercial software packages on twenty randomly-selected images. Additionally, inter-observer variability (IOV) of contours between three radiation oncology experts and the original contours was evaluated on ten randomly-selected images. The highest median values of DSC across all institutions were 0.94-0.97 for bladder (with in...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research