Fig. 1. Structure of the RDB

Fig. 2. Dilated convolution operation of r=2 is performed in sequence. (a) First time; (b) second time; (c) third time

Fig. 3. Segmentation results of the dilated convolution. (a) Original image; (b) ground truth; (c) grid artifact

Fig. 4. Dilated convolution operations with different dilated rates in sequence. (a) r=1; (b) r=2; (c) r=3

Fig. 5. Structure of the TASD module

Fig. 6. Framework of the segmentation network

Fig. 7. Manually annotated image data

Fig. 8. Result of image enhancement. (a) Original image; (b) flip up and down; (c) flip left and right; (d) clockwise rotate 90°; (e) counterclockwise rotate 90°; (f) random zoom and rotate 1; (g) random zoom and rotate 2; (h) random room and rotate 3

Fig. 9. Statistics of the tumor size

Fig. 10. Segmentation results of our algorithm and traditional segmentation algorithm. (a) Original image; (b) region growth; (c) graph cut segmentation; (d) level set segmentation; (e) our algorithm; (g) ground truth

Fig. 11. Segmentation results of our algorithm and deep learning segmentation algorithm. (a) Original image; (b) UNet; (c) SegNet; (d) DeepLabv3; (e) FC-DenseNet; (f) our algorithm; (g) ground truth

Fig. 12. Effect of different modules on segmentation performance. (a) Original image; (b) CEL function; (c) remove RDB; (d) remove TASD module; (e) our algorithm; (f) ground truth