TY - GEN
T1 - Does manual delineation only provide the side information in CT prostate segmentation?
AU - Shi, Yinghuan
AU - Yang, Wanqi
AU - Gao, Yang
AU - Shen, Dinggang
N1 - Funding Information:
Acknowledgement. This work was supported by NSFC (61673203, 61432008, 61603193), NIH Grant (CA206100), and Young Elite Scientists Sponsorship Program by CAST (YESS 20160035).
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029525475&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-66179-7_79
DO - 10.1007/978-3-319-66179-7_79
M3 - Conference contribution
AN - SCOPUS:85029525475
SN - 9783319661780
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 692
EP - 700
BT - Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
A2 - Maier-Hein, Lena
A2 - Franz, Alfred
A2 - Jannin, Pierre
A2 - Duchesne, Simon
A2 - Descoteaux, Maxime
A2 - Collins, D. Louis
PB - Springer Verlag
T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Y2 - 11 September 2017 through 13 September 2017
ER -