Discrete Deformable Model Guided by Partial Active Shape Model for TRUS Image Segmentation

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 5; pp. 1158 - 1166
• Main Authors: Yan, Pingkun, Xu, Sheng, Turkbey, Baris, Kruecker, Jochen
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active shape model; Algorithms; Biological and medical sciences; Biopsy; Boundaries; Cancer; Computed tomography; Computer Simulation; Deformable models; Discrete deformable model (DDM); Dynamic programming; Genital system. Mammary gland; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Medical treatment; Models, Biological; partial active shape model (PASM); Pattern Recognition, Automated - methods; prostate; Prostate - diagnostic imaging; Prostatic Neoplasms - diagnostic imaging; Reproducibility of Results; Robustness; Screen printing; Sensitivity and Specificity; Studies; transrectal ultrasound (TRUS); Ultrasonic imaging; Ultrasonic investigative techniques; Ultrasonography - methods; Sonography; Human; Segmentation; Image processing; partial active shape model (PASM); dynamic programming; Male; Algorithm; Modeling; Automatic processing; Discrete deformable model (DDM); image segmentation; Statistical model; Echography; transrectal ultrasound (TRUS); Urogenital system; Prostate; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2009.2037491

Automatic prostate segmentation in transrectal ultrasound (TRUS) images is highly desired in many clinical applications. However, robust and automated prostate segmentation is challenging due to the low SNR in TRUS and the missing boundaries in shadow areas caused by calcifications or hyperdense prostate tissues. This paper presents a novel method of utilizing a priori shapes estimated from partial contours for segmenting the prostate. The proposed method is able to automatically extract prostate boundary from 2-D TRUS images without user interaction for shape correction in shadow areas. During the segmentation process, missing boundaries in shadow areas are estimated by using a partial active shape model, which takes partial contours as input but returns a complete shape estimation. With this shape guidance, an optimal search is performed by a discrete deformable model to minimize an energy functional for image segmentation, which is achieved efficiently by using dynamic programming. The segmentation of an image is executed in a multiresolution fashion from coarse to fine for robustness and computational efficiency. Promising segmentation results were demonstrated on 301 TRUS images grabbed from 19 patients with the average mean absolute distance error of 2.01 mm ± 1.02 mm.