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

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Accurate prostate segmentation in CT images is crucial for localization but challenging due to low contrast and variable appearance.
• Distinguishing the prostate from surrounding tissues requires informative feature representations.

Purpose of the Study:

• To develop a novel method for enhancing feature learning in prostate segmentation.
• To improve the accuracy of prostate localization in CT images using deep learning.

Main Methods:

• Proposed a cascaded deep domain adaptation (CDDA) model that uses manual delineations to create multiple source domains.
• Employed different mask ratios to overlay manual delineations on CT images, guiding feature learning.
• Implemented two variations: CDDA-CNN (patch-to-scalar) and CDDA-FCN (patch-to-patch).
• Theoretically analyzed the generalization error bound of the CDDA model.

Main Results:

• The CDDA model demonstrated promising results in prostate segmentation.
• The proposed method effectively learns transferrable features for improved prostate localization.
• Experimental results validated the efficacy of both CDDA-CNN and CDDA-FCN.

Conclusions:

• The CDDA model offers an effective approach for challenging prostate segmentation tasks.
• Leveraging manual delineations within a domain adaptation framework enhances feature learning.
• The method shows significant potential for improving prostate localization in clinical settings.