Dataset Information

CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy.

ABSTRACT: Purpose: More and more automatic segmentation tools are being introduced in routine clinical practice. However, physicians need to spend a considerable amount of time in examining the generated contours slice by slice. This greatly reduces the benefit of the tool's automaticity. In order to overcome this shortcoming, we developed an automatic quality assurance (QA) method for automatic segmentation using convolutional neural networks (CNNs). Materials and Methods: The study cohort comprised 680 patients with early-stage breast cancer who received whole breast radiation. The overall architecture of the automatic QA method for deep learning-based segmentation included the following two main parts: a segmentation CNN model and a QA network that was established based on ResNet-101. The inputs were from computed tomography, segmentation probability maps, and uncertainty maps. Two kinds of Dice similarity coefficient (DSC) outputs were tested. One predicted the DSC quality level of each slice ([0.95, 1] for "good," [0.8, 0.95] for "medium," and [0, 0.8] for "bad" quality), and the other predicted the DSC value of each slice directly. The performances of the method to predict the quality levels were evaluated with quantitative metrics: balanced accuracy, F score, and the area under the receiving operator characteristic curve (AUC). The mean absolute error (MAE) was used to evaluate the DSC value outputs. Results: The proposed methods involved two types of output, both of which achieved promising accuracy in terms of predicting the quality level. For the good, medium, and bad quality level prediction, the balanced accuracy was 0.97, 0.94, and 0.89, respectively; the F score was 0.98, 0.91, and 0.81, respectively; and the AUC was 0.96, 0.93, and 0.88, respectively. For the DSC value prediction, the MAE was 0.06 ± 0.19. The prediction time was approximately 2 s per patient. Conclusions: Our method could predict the segmentation quality automatically. It can provide useful information for physicians regarding further verification and revision of automatic contours. The integration of our method into current automatic segmentation pipelines can improve the efficiency of radiotherapy contouring.

SUBMITTER: Chen X

PROVIDER: S-EPMC7212344 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy.

Chen Xinyuan X Men Kuo K Chen Bo B Tang Yu Y Zhang Tao T Wang Shulian S Li Yexiong Y Dai Jianrong J

Frontiers in oncology 20200428

<b>Purpose:</b> More and more automatic segmentation tools are being introduced in routine clinical practice. However, physicians need to spend a considerable amount of time in examining the generated contours slice by slice. This greatly reduces the benefit of the tool's automaticity. In order to overcome this shortcoming, we developed an automatic quality assurance (QA) method for automatic segmentation using convolutional neural networks (CNNs). <b>Materials and Methods:</b> The study cohort ...[more]

PMID: 32426272

Similar Datasets

Project description:PurposeManually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD). However, manual tracing is resource intensive. To address these limitations and to expand the availability of a quantitative CT measure of hepatic steatosis, we propose the automatic liver attenuation ROI-based measurement (ALARM) method for automated liver attenuation estimation.MethodsThe ALARM method consists of two major stages: (a) deep convolutional neural network (DCNN)-based liver segmentation and (b) automated ROI extraction. First, liver segmentation was achieved using our previously developed SS-Net. Then, a single central ROI (center-ROI) and three circles ROI (periphery-ROI) were computed based on liver segmentation and morphological operations. The ALARM method is available as an open source Docker container (https://github.com/MASILab/ALARM).ResultsTwo hundred and forty-six subjects with 738 abdomen CT scans from the African American-Diabetes Heart Study (AA-DHS) were used for external validation (testing), independent from the training and validation cohort (100 clinically acquired CT abdominal scans). From the correlation analyses, the proposed ALARM method achieved Pearson correlations = 0.94 with manual estimation on liver attenuation estimations. When evaluating the ALARM method for detection of nonalcoholic fatty liver disease (NAFLD) using the traditional cut point of < 40 HU, the center-ROI achieved substantial agreements (Kappa = 0.79) with manual estimation, while the periphery-ROI method achieved "excellent" agreement (Kappa = 0.88) with manual estimation. The automated ALARM method had reduced variability compared to manual measurements as indicated by a smaller standard deviation.ConclusionsWe propose a fully automated liver attenuation estimation method termed ALARM by combining DCNN and morphological operations, which achieved "excellent" agreement with manual estimation for fatty liver detection. The entire pipeline is implemented as a Docker container which enables users to achieve liver attenuation estimation in five minutes per CT exam.

Project description:Purpose: Ensuring high-quality data for clinical trials in radiotherapy requires the generation of contours that comply with protocol definitions. The current workflow includes a manual review of the submitted contours, which is time-consuming and subjective. In this study, we developed an automated quality assurance (QA) system for lung cancer based on a segmentation model trained with deep active learning. Methods: The data included a gold atlas with 36 cases and 110 cases from the "NRG Oncology/RTOG 1308 Trial". The first 70 cases enrolled to the RTOG 1308 formed the candidate set, and the remaining 40 cases were randomly assigned to validation and test sets (each with 20 cases). The organs-at-risk included the heart, esophagus, spinal cord, and lungs. A preliminary convolutional neural network segmentation model was trained with the gold standard atlas. To address the deficiency of the limited training data, we selected quality images from the candidate set to be added to the training set for fine-tuning of the model with deep active learning. The trained robust segmentation models were used for QA purposes. The segmentation evaluation metrics derived from the validation set, including the Dice and Hausdorff distance, were used to develop the criteria for QA decision making. The performance of the strategy was assessed using the test set. Results: The QA method achieved promising contouring error detection, with the following metrics for the heart, esophagus, spinal cord, left lung, and right lung: balanced accuracy, 0.96, 0.95, 0.96, 0.97, and 0.97, respectively; sensitivity, 0.95, 0.98, 0.96, 1.0, and 1.0, respectively; specificity, 0.98, 0.92, 0.97, 0.94, and 0.94, respectively; and area under the receiving operator characteristic curve, 0.96, 0.95, 0.96, 0.97, and 0.94, respectively. Conclusions: The proposed system automatically detected contour errors for QA. It could provide consistent and objective evaluations with much reduced investigator intervention in multicenter clinical trials.

Project description:Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps' dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.

Dataset Information

CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy.

Publications

CNN-Based Quality Assurance for Automatic Segmentation of Breast Cancer in Radiotherapy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets