Combining population and patient-specific characteristics for prostate segmentation on 3D CT images

Ling Ma, Rongrong Guo, Zhiqiang Tian, Rajesh Venkataraman, Saradwata Sarkar, Xiabi Liu, Funmilayo Tade, David M. Schuster, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2016
Subtitle of host publicationImage Processing
EditorsMartin A. Styner, Elsa D. Angelini, Elsa D. Angelini
PublisherSPIE
ISBN (Electronic)9781510600195
DOIs
StatePublished - 2016
Externally publishedYes
EventMedical Imaging 2016: Image Processing - San Diego, United States
Duration: Mar 1 2016Mar 3 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9784
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2016: Image Processing
CountryUnited States
CitySan Diego
Period3/1/163/3/16

Keywords

  • CT
  • Image segmentation
  • Patient-specific characteristics
  • Population characteristics
  • Prostate

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Ma, L., Guo, R., Tian, Z., Venkataraman, R., Sarkar, S., Liu, X., Tade, F., Schuster, D. M., & Fei, B. (2016). Combining population and patient-specific characteristics for prostate segmentation on 3D CT images. In M. A. Styner, E. D. Angelini, & E. D. Angelini (Eds.), Medical Imaging 2016: Image Processing [978427] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 9784). SPIE. https://doi.org/10.1117/12.2216255