Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy

Abstract

Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours’ location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices.

A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization

“…To tackle this challenge, many researchers have used different methods to automate the process. In previous studies, we (Luan et al 2023c) and other researchers (Claessens et al 2022b, Duan et al 2023 have employed machine learning or deep learning techniques for QA of error contours. However, all these studies have used binary classification approaches (pass/fail) and have not provided specific details about the location and reasons for contouring errors.…”

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN

“…Deep learning techniques, specifically CNNs, have been applied to various medical image analyses [ 8 , 9 , 10 , 11 ]. CNNs are widely used for image classification [ 12 , 13 , 14 ] regression [ 15 , 16 , 17 ], object detection [ 18 , 19 ], super resolution [ 20 , 21 ], and semantic segmentation [ 22 , 23 , 24 ]. Recent studies have proposed automatic segmentation of the left ventricle lumen to reduce tracing time and interobserver errors in the study of cardiac function [ 25 ].…”

Novel Applications of Artificial Intelligence in Cancer Research

“…Luan et al 8 used CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models.…”