Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy

Abstract

Purpose: Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning.Material and methods: U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95 th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared.Results: The averaged overall 10 test patients' DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D 90% , and less than 2.64% for organs at risk D 2cc at a scenario of doubled maximum shaft error.Conclusions: We proposed a deep learning-based reconstruction method to localize Fletcher applicator in threedimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.

Automatic reconstruction of interstitial needles using CT images in post-operative cervical cancer brachytherapy based on deep learning

“…A DSD-U-Net model [26] was proposed to reconstruct the intra-uterine and ovoid tubes, and achieved average DSC value of 0.92. A U-Net model [32] was used to automatically segment Fletcher applicator with average DSC value of 0.89. A 2D U-Net algorithm [33] was tested to reconstruct the needles, with average DSC value of 0.59 and HD value of 4.2 mm, based on MR images.…”

Comparison of failure modes and effects analyses and time for brachytherapy ring and tandem applicator digitization between manual and solid applicator source placement methods

“…Due to the unclear superiority of a particular method, AAPM/GEC‐ESTRO Task Group‐236 is currently involved in developing a workflow and consensus on intracavitary brachytherapy applicator digitization among other aims 3–5 . Further, some brachytherapy teams are exploring novel approaches beyond the well‐documented digitization methods to offer a superior option 6–8 . As these approaches are not available presently, this study focuses on the manual and solid applicator digitization methods.…”

“… 3 , 4 , 5 Further, some brachytherapy teams are exploring novel approaches beyond the well‐documented digitization methods to offer a superior option. 6 , 7 , 8 As these approaches are not available presently, this study focuses on the manual and solid applicator digitization methods.…”

Self‐configuring nnU‐Net for automatic delineation of the organs at risk and target in high‐dose rate cervical brachytherapy, a low/middle‐income country's experience

“…36,37 DL has also been effectively applied in various areas of brachytherapy from applicator reconstruction, dose calculation, treatment planning as well as organ delineation. 38 In IG-HDR cervical brachytherapy, work has been done on automating the reconstruction of applicators using DL, 39,40 with a few studies looking at the automatic segmentation of the OARs and targets. [40][41][42][43][44] These studies however do not look at the clinical acceptability of the generated contours and involve complex deep learning models that require a high level of expertise to reproduce or apply in one's own clinical department.…”

“…DL has also been effectively applied in various areas of brachytherapy from applicator reconstruction, dose calculation, treatment planning as well as organ delineation 38 . In IG‐HDR cervical brachytherapy, work has been done on automating the reconstruction of applicators using DL, 39,40 with a few studies looking at the automatic segmentation of the OARs and targets 40–44 …”