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ORIGINAL ARTICLES: RADIOTHERAPY AND RADIOBIOLOGY

Automatic segmentation of pelvic organs-at-risk using a fusion network model based on limited training samples

, , , , , , , , & show all
Pages 933-939 | Received 19 Mar 2020, Accepted 23 May 2020, Published online: 22 Jun 2020

Figures & data

Figure 1. The structure of Networks. (a) Dense Net, (b) V-Net, (c) Dense V-Network.

Figure 1. The structure of Networks. (a) Dense Net, (b) V-Net, (c) Dense V-Network.

Figure 2. The model training process.

Figure 2. The model training process.

Figure 3. The box plot of three evaluation parameter results.

Figure 3. The box plot of three evaluation parameter results.

Table 1. Segmentation results of pelvic organs at risk (Mean).

Figure 4. The automatic segmentation result of CT images. (a) Bladder, (b) intestine, (c) rectum, (d) femur, (e) cord.

Figure 4. The automatic segmentation result of CT images. (a) Bladder, (b) intestine, (c) rectum, (d) femur, (e) cord.
Supplemental material

Supplemental Material

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