Aug 15, 2018

Pelvic Organ Segmentation Using Distinctive Curve Guided Fully Convolutional Networks

IEEE Transactions on Medical Imaging
Kelei HeDinggang Shen


Accurate segmentation of pelvic organs (i.e., prostate, bladder and rectum) from CT image is crucial for effective prostate cancer radiotherapy. However, it is a challenging task due to 1) low soft tissue contrast in CT images and 2) large shape and appearance variations of pelvic organs. In this paper, we employ a two-stage deep learning based method, with a novel distinctive curve guided fully convolutional network (FCN), to solve the aforementioned challenges. Specifically, the first stage is for fast and robust organ detection in the raw CT images. It is designed as a coarse segmentation network to provide region proposals for three pelvic organs. The second stage is for fine segmentation of each organ, based on the region proposal results. To better identify those indistinguishable pelvic organ boundaries, a novel morphological representation, namely distinctive curve, is also introduced to help better conduct the precise segmentation. To implement this, in this second stage, a multi-task FCN is initially utilized to learn the distinctive curve and the segmentation map separately, and then combine these two tasks to produce accurate segmentation map. The final segmentation results of all three pelvic organs are generated b...Continue Reading

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Mentioned in this Paper

Prostatic Neoplasms
Biologic Segmentation
Entire Rectum
Neoplasm of Uncertain or Unknown Behavior of Bladder
Neoplasm of Uncertain or Unknown Behavior of Rectum
Carcinoma in Situ of Bladder
Bladder Tissue
Prostate Carcinoma

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