AccuLearning: A User-Friendly Deep Learning Auto-Segmentation Platform for Radiotherapy.

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Abstract

Despite the significant success of deep learning (DL) techniques in auto-segmentation of medical images, the application is often limited by various technical difficulties which are costly to implement for medical researchers without any programming experience. To address these issues, we developed a user-friendly deep learning platform, i.e., AccuLearning, to provide accessible DL resources for clinical application to researchers in medical field.The goal of AccuLearning is to simplify the DL model building workflow, hide the complexities of model training, and provide highly integrated, efficient and flexible platform for the implementation of deep learning auto-segmentation technique. To do this, all the major components were integrated into an automatic pipeline. Gradient-based adaptive network structure building and targeted loss function make the training process of the model easier and more stable. To make the platform generalizable, adaptive image pre-processing strategies were employed, such as automatic dataset characteristics extraction and standardization. With an easy-to-use graphical user interface and optimized default parameters, minimal user intervention is needed. To demonstrate the effectiveness, we applied AccuLearning on a total of 9 auto-segmentation tasks using open-access medical image datasets, covering 64 ROIs (including 4 GTVs and 60 OARs) on various anatomy sites (abdomen, head and neck, and pelvis) on CTs or MRIs. The model performance was evaluated using Dice and HD95 compared with ground truth. In addition, the impact of number of training cases were also evaluated.AccuLearning can achieve competitive model accuracy using default settings, with less than 3 hours of training time and 45 seconds of average inference time per patient. The average Dice and HD were 0.81 ± 0.11 and 7.7 ± 7.4mm for all the OARs. 18.3% (11/60) of the OARs have Dice larger than 0.9, while 66.7% (40/60) larger than 0.8. For HD95, 26.7% (16/60) of the OARs have HD95 smaller than 3mm and 73.3% (44/60) smaller than 10mm. The DL model trained with only 20 cases showed no significant difference with the model trained with 160 cases in OARs segmentation. For nasopharyngeal carcinoma GTV auto-segmentation, the average Dice was 0.62. Through adjustments to model dimensions, loss function and learning rate schedule, the average Dice for GTV was increased to 0.69, which is reasonably comparable to the inter-observer variations Dice 0.71.AccuLearning can build robust and accurate DL models for different auto-segmentation tasks on both CT and MRI with minimal user interventions, short model training time and low demand of training sample size. The platform can be implemented into current workflow, enabling fast and accurate target and OARs segmentation for clinical practice in radiotherapy field.Copyright © 2021. Published by Elsevier Inc.