# CDA-Net for Automatic Prostate Segmentation in MR Images
## 5 Conclusions  
Reliable prostate contour segmentation is essential for radiotherapy planning for prostate cancer. In order to improve the time-consuming problem of slice-by-slice visual inspection by traditional radiologists and the inter- and intraobserver difference, this paper presents a cascaded dual attention network CDA-Net for the fully automatic segmentation of prostate MR images. Taking the flexibility and robustness of the stage-wise segmentation algorithm into account, we firstly used the RAS-FasterRCNN method to realize the organ localization of the 2D slice image, and secondly employed the RAU-Net method to further realize the accurate segmentation of the prostate organ based on the ROI extraction. (1) Due to the anisotropy of the prostate image, that is, the layer spacing between the axial slices is large, taking advantage of the weak correlation between the slices, this paper introduces sequence correlation processing based on spatial curve fitting to the back end of RA-FasterRCNN to improve poorly detected images. After the correlation processing, the success rate of organ localization on PROMISE12 and ASPS13 test sets reached 99.78% and 99.88%, respectively. (2) In addition, we introduced residual connections based on U-Net to fuse the shallow and deep features of different levels. Channel splicing based on the attention gating mechanism is also added to highlight the network's attention to the foreground organ area, suppress the feature activation of the background area and noise points, and effectively improve the pixel-level prediction accuracy of the network. The experimental results show that exploiting only 40 training samples, the CDA-Net proposed in this paper achieved a mean dice score of 92.88% and 92.65% on the PROMISE12 test set with high heterogeneity and the ASPS13 dataset with larger scale. Similarly, in the visualization experiment, the algorithm has significantly improved the prediction effect of organ segmentation for difficult images such as obvious uneven gray distribution inside glands and blurred organ boundaries, especially for slice images at both ends of the sequence. It can be concluded that the advantages of the step-by-step attention focusing mechanism introduced by the algorithm are indeed convincing. In future work, we will further improve the loss function settings of the segmentation algorithm, such as introducing boundary-based loss functions or cross-entropy loss function or combined loss functions, hoping to further improve the algorithm's prediction effect on gland contours. Conceptualization, Z.L. and M.Z. ; methodology, M.Z. ; software, M.Z. ; validation, M.Z. and Y.P. ; formal analysis, Z.L. and M.Z. ; investigation, Z.L., M.Z. and Y.P. ; resources, Z.L. ; data curation, M.Z. ; writing--original draft preparation, M.Z. ; writing--review and editing, Z.L. and Y.P. ; visualization, M.Z. ; supervision, Z.L. ; project administration, Z.L. and Y.P. ; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China under Grant 51677123. We appreciate the organizers of the 2012 prostate MR image segmentation challenge for sharing the data set and opening up various method rankings and test results. At the same time, we are grateful to the organizers of the 2013 Prostate Structure Segmentation Challenge for their efforts in collecting and sharing data sets. The authors declare that there is no conflict of interest regarding the publication of this paper. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Semi-Automatic Segmentation of Prostate in CT Images via Coupled Feature Representation and Spatial-Constrained Transductive Lasso. _IEEE Trans. Pattern Anal. Mach. Intell._**2015**, _37_, 2286-2303. [CrossRef] [PubMed]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON] Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. _IEEE Trans. Med. Imaging_**2015**, _35_, 1077-1089.