Faculty Collaboration Database

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging. Int J Radiat Oncol Biol Phys 2022 Oct 01;114(2):349-359

Date

06/07/2022

Pubmed ID

35667525

Pubmed Central ID

PMC9639200

DOI

10.1016/j.ijrobp.2022.05.039

Scopus ID

2-s2.0-85136545309 (requires institutional sign-in at Scopus site)   4 Citations

Abstract

PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans.

METHODS AND MATERIALS: A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 patients (65 for training and 10 for testing). The DL-SAS model performance was compared with 3 common autocontouring methods (linear interpolation, rigid propagation, and a full 3-dimensional DL autosegmentation model trained with the same training data set) based on various quantitative metrics including the Dice similarity coefficient (DSC) and ratio of acceptable slices (ROA) using paired t tests.

RESULTS: For the 10 testing cases, the DL-SAS model performed best with the slice interval (SI) of 1, resulting in an average DSC of 0.93 ± 0.02, 0.92 ± 0.02, 0.91 ± 0.02, 0.88 ± 0.03, and 0.87 ± 0.02 for the large bowel, stomach, small bowel, duodenum, and pancreas, respectively. The performance decreased with increased SIs from the guidance slice. The DL-SAS method performed significantly better (P < .05) than the other 3 methods. The ROA values were in the range of 48% to 66% for all the organs with an SI of 1 for DL-SAS, higher than those for linear interpolation (31%-57% for an SI of 1) and DL auto-segmentation (16%-51%).

CONCLUSIONS: The developed DL-SAS model segmented complex abdominal structures on MRI with high accuracy and efficiency and may be implemented as an interactive manual contouring tool or a contour editing tool in conjunction with a full autosegmentation process, facilitating fast and accurate segmentation for MRI-guided online adaptive radiation therapy.

Author List

Zhang Y, Liang Y, Ding J, Amjad A, Paulson E, Ahunbay E, Hall WA, Erickson B, Li XA

Authors

Ergun Ahunbay PhD Professor in the Radiation Oncology department at Medical College of Wisconsin
Asma Amjad PhD Medical Physicist Assistant in the Radiation Oncology department at Medical College of Wisconsin
Beth A. Erickson MD Professor in the Radiation Oncology department at Medical College of Wisconsin
William Adrian Hall MD Professor in the Radiation Oncology department at Medical College of Wisconsin
Eric Paulson PhD Chief, Professor in the Radiation Oncology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Radiotherapy, Image-Guided