Fully automated segmentation of clinical target volume in cervical cancer from magnetic resonance imaging with convolutional neural network. Issue 9 (27th July 2022)

• Record Type: Journal Article
• Authors: Zabihollahy, Fatemeh; Viswanathan, Akila N.; Schmidt, Ehud J.; Lee, Junghoon
• Is Part Of: Journal of applied clinical medical physics. Volume 23:Issue 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-27
• Subjects: cervical cancer -- clinical target volume -- deep learning segmentation -- magnetic resonance imaging -- radiotherapy; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.13725
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: Contouring clinical target volume (CTV) from medical images is an essential step for radiotherapy (RT) planning. Magnetic resonance imaging (MRI) is used as a standard imaging modality for CTV segmentation in cervical cancer due to its superior soft‐tissue contrast. However, the delineation of CTV is challenging as CTV contains microscopic extensions that are not clearly visible even in MR images, resulting in significant contour variability among radiation oncologists depending on their knowledge and experience. In this study, we propose a fully automated deep learning–based method to segment CTV from MR images. Methods: Our method begins with the bladder segmentation, from which the CTV position is estimated in the axial view. The superior–inferior CTV span is then detected using an Attention U‐Net. A CTV‐specific region of interest (ROI) is determined, and three‐dimensional (3‐D) blocks are extracted from the ROI volume. Finally, a CTV segmentation map is computed using a 3‐D U‐Net from the extracted 3‐D blocks. Results: We developed and evaluated our method using 213 MRI scans obtained from 125 patients (183 for training, 30 for test). Our method achieved (mean ± SD) Dice similarity coefficient of 0.85 ± 0.03 and the 95th percentile Hausdorff distance of 3.70 ± 0.35 mm on test cases, outperforming other state‐of‐the‐art methods significantly ( p ‐value < 0.05). Our method also produces an uncertainty map along with the CTV segmentation by employing the Monte Carlo dropout technique to draw physician's attention to the regions with high uncertainty, where careful review and manual correction may be needed. Conclusions: Experimental results show that the developed method is accurate, fast, and reproducible for contouring CTV from MRI, demonstrating its potential to assist radiation oncologists in alleviating the burden of tedious contouring for RT planning in cervical cancer.