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Original Research

Automatic Segmentation of Clinical Target Volume and Organs-at-Risk for Breast Conservative Radiotherapy Using a Convolutional Neural Network

ORCID Icon, , , ORCID Icon, , ORCID Icon, ORCID Icon, , , , , & ORCID Icon show all
Pages 8209-8217 | Published online: 02 Nov 2021

Figures & data

Table 1 The Standard Delineation of CTV After BCS

Figure 1 Architecture of (A) deep dilated convolutional neural network (DDCNN), (B) our proposed network, and (C) the residual block used in decoder part of our network.

Figure 1 Architecture of (A) deep dilated convolutional neural network (DDCNN), (B) our proposed network, and (C) the residual block used in decoder part of our network.

Table 2 DSC and 95HD for CTV and All OARs

Figure 2 Boxplots obtained for DSC and 95HD analyses of U-ResNet and U-Net. (A) DSC analyses, (B) 95HD analyses.

Figure 2 Boxplots obtained for DSC and 95HD analyses of U-ResNet and U-Net. (A) DSC analyses, (B) 95HD analyses.

Figure 3 CTV and OAR contours generated by (A) GT, (B) U-ResNet, and (C) U-Net after breast conservative surgery.

Figure 3 CTV and OAR contours generated by (A) GT, (B) U-ResNet, and (C) U-Net after breast conservative surgery.

Table 3 Evaluation for CTV and OARs by Oncologist A

Table 4 Evaluation for CTV and OARs by Oncologist B

Figure 4 The average CTV scores evaluated by two oncologists.

Figure 4 The average CTV scores evaluated by two oncologists.

Table 5 The Consistency Test Between Two Oncologists