Dataset Information

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

ABSTRACT:

Background and purpose

We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy.

Material and methods

We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparing cardiac doses for simulated breast radiotherapy between manual and automatic contours. We analyzed the impact of the number of cardiac atlas in the library and the use of manual guide points on the performance of our method.

Results

The Dice Similarity Coefficients from the cross-validation reached up to 97% (whole heart) and 80% (chambers). The Average Surface Distance for the coronary arteries was less than 10.3 mm on average, with the best agreement (7.3 mm) in the left anterior descending artery (LAD). The dose comparison for simulated breast radiotherapy showed differences less than 0.06 Gy for the whole heart and atria, and 0.3 Gy for the ventricles. For the coronary arteries, the dose differences were 2.3 Gy (LAD) and 0.3 Gy (other arteries). The sensitivity analysis showed no notable improvement beyond ten atlases and the manual guide points does not significantly improve performance.

Conclusion

We developed an automated method to contour cardiac substructures for radiotherapy CTs. When combined with accurate dose calculation techniques, our method should be useful for cardiac dose reconstruction of a large number of patients in epidemiological studies or clinical trials.

SUBMITTER: Jung JW

PROVIDER: S-EPMC7807574 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

Jung Jae Won JW Lee Choonik C Mosher Elizabeth G EG Mille Matthew M MM Yeom Yeon Soo YS Jones Elizabeth C EC Choi Minsoo M Lee Choonsik C

Physics and imaging in radiation oncology 20191001

<h4>Background and purpose</h4>We developed an automatic method to segment cardiac substructures given a radiotherapy planning CT images to support epidemiological studies or clinical trials looking at cardiac disease endpoints after radiotherapy.<h4>Material and methods</h4>We used a most-similar atlas selection algorithm and 3D deformation combined with 30 detailed cardiac atlases. We cross-validated our method within the atlas library by evaluating geometric comparison metrics and by comparin ...[more]

PMID: 33458294

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Project description: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.

Dataset Information

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

Background and purpose

Material and methods

Results

Conclusion

Publications

Automatic segmentation of cardiac structures for breast cancer radiotherapy.

Similar Datasets

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