Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer

Abstract

Objectives: Accurate contouring of the clinical target volume (CTV) is a key element of radiotherapy in cervical cancer. We validated a novel deep learning (DL)-based auto-segmentation algorithm for CTVs in cervical cancer called the three-channel adaptive auto-segmentation network (TCAS).

Methods: A total of 107 cases were collected and contoured by senior radiation oncologists (ROs). Each case consisted of the following: (1) contrast-enhanced CT scan for positioning, (2) the related CTV, (3) multiple plain CT scans during treatment and (4) the related CTV. After registration between (1) and (3) for the same patient, the aligned image and CTV were generated. Method 1 is rigid registration, method 2 is deformable registration, and the aligned CTV is seen as the result. Method 3 is rigid registration and TCAS, method 4 is deformable registration and TCAS, and the result is generated by a DL-based method.

Results: From the 107 cases, 15 pairs were selected as the test set. The dice similarity coefficient (DSC) of method 1 was 0.8155 ± 0.0368; the DSC of method 2 was 0.8277 ± 0.0315; the DSCs of method 3 and 4 were 0.8914 ± 0.0294 and 0.8921 ± 0.0231, respectively. The mean surface distance and Hausdorff distance of methods 3 and 4 were markedly better than those of method 1 and 2.

Conclusions: The TCAS achieved comparable accuracy to the manual delineation performed by senior ROs and was significantly better than direct registration.

Keywords: Auto-segmentation; Cervical cancer CTV; Deep learning; Registration.

Fig. 1

Details of the CT datasets

Fig. 2

Difference of plain CT (left) and contrast-enhanced CT (right)

Fig. 3

Workflows of four methods

Fig. 4

The structure of VB-Net

Fig. 5

Contour of a representative test case using the five methods. Each column represents a different slice. The ground truth is in red, and the contour using the different methods is in the indicated colors. RR: Green, DR: blue, TCAS + RR: magenta, TCAS + DR: cyan