Prostate segmentation, for accurate prostate localization in CT images, is regarded as a crucial yet challenging task. Nevertheless, due to the inevitable factors (e.g., low contrast, large appearance and shape changes), the most important problem is how to learn the informative feature representation to distinguish the prostate from non-prostate regions. We address this challenging feature learning by leveraging the manual delineation as guidance: the manual delineation does not only indicate the category of patches, but also helps enhance the appearance of prostate. This is realized by the proposed cascaded deep domain adaptation (CDDA) model. Specifically, CDDA constructs several consecutive source domains by employing a mask of manual delineation to overlay on the original CT images with different mask ratios. Upon these source domains, convnet will guide better transferrable feature learning until to the target domain. Particularly, we implement two typical methods: patch-to-scalar (CDDA-CNN) and patch-to-patch (CDDA-FCN). Also, we theoretically analyze the generalization error bound of CDDA. Experimental results show the promising results of our method.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings|
|Editors||Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins|
|Number of pages||9|
|Publication status||Published - 2017|
|Event||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada|
Duration: 2017 Sept 11 → 2017 Sept 13
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017|
|Period||17/9/11 → 17/9/13|
Bibliographical noteFunding Information:
Acknowledgement. This work was supported by NSFC (61673203, 61432008, 61603193), NIH Grant (CA206100), and Young Elite Scientists Sponsorship Program by CAST (YESS 20160035).
© Springer International Publishing AG 2017.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)