Does Manual Delineation only Provide the Side Information in CT Prostate Segmentation?

  • Yinghuan ShiEmail author
  • Wanqi Yang
  • Yang Gao
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


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.



This work was supported by NSFC (61673203, 61432008, 61603193), NIH Grant (CA206100), and Young Elite Scientists Sponsorship Program by CAST (YESS 20160035).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yinghuan Shi
    • 1
    Email author
  • Wanqi Yang
    • 1
    • 2
  • Yang Gao
    • 1
  • Dinggang Shen
    • 3
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Computer ScienceNanjing Normal UniversityNanjingChina
  3. 3.Department of Radiology and BRICUNC Chapel HillChapel HillUSA

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