21 March 2016 Combining population and patient-specific characteristics for prostate segmentation on 3D CT images
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Abstract
Prostate segmentation on CT images is a challenging task. In this paper, we explore the population and patient-specific characteristics for the segmentation of the prostate on CT images. Because population learning does not consider the inter-patient variations and because patient-specific learning may not perform well for different patients, we are combining the population and patient-specific information to improve segmentation performance. Specifically, we train a population model based on the population data and train a patient-specific model based on the manual segmentation on three slice of the new patient. We compute the similarity between the two models to explore the influence of applicable population knowledge on the specific patient. By combining the patient-specific knowledge with the influence, we can capture the population and patient-specific characteristics to calculate the probability of a pixel belonging to the prostate. Finally, we smooth the prostate surface according to the prostate-density value of the pixels in the distance transform image. We conducted the leave-one-out validation experiments on a set of CT volumes from 15 patients. Manual segmentation results from a radiologist serve as the gold standard for the evaluation. Experimental results show that our method achieved an average DSC of 85.1% as compared to the manual segmentation gold standard. This method outperformed the population learning method and the patient-specific learning approach alone. The CT segmentation method can have various applications in prostate cancer diagnosis and therapy.
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Ling Ma, Rongrong Guo, Zhiqiang Tian, Rajesh Venkataraman, Saradwata Sarkar, Xiabi Liu, Funmilayo Tade, David M. Schuster, Baowei Fei, "Combining population and patient-specific characteristics for prostate segmentation on 3D CT images", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978427 (21 March 2016); doi: 10.1117/12.2216255; https://doi.org/10.1117/12.2216255
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