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Prostate boundary segmentation from 3D ultrasound images
Author(s) -
Hu Ning,
Downey Dónal B.,
Fenster Aaron,
Ladak Hanif M.
Publication year - 2003
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.1586267
Subject(s) - segmentation , initialization , computer science , artificial intelligence , boundary (topology) , image segmentation , computer vision , pattern recognition (psychology) , algorithm , mathematics , mathematical analysis , programming language
Segmenting, or outlining the prostate boundary is an important task in the management of patients with prostate cancer. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 3D ultrasound images. The algorithm uses model‐based initialization and mesh refinement using an efficient deformable model. Initialization requires the user to select only six points from which the outline of the prostate is estimated using shape information. The estimated outline is then automatically deformed to better fit the prostate boundary. An editing tool allows the user to edit the boundary in problematic regions and then deform the model again to improve the final results. The algorithm requires less than 1 min on a Pentium III 400 MHz PC. The accuracy of the algorithm was assessed by comparing the algorithm results, obtained from both local and global analysis, to the manual segmentations on six prostates. The local difference was mapped on the surface of the algorithm boundary to produce a visual representation. Global error analysis showed that the average difference between manual and algorithm boundaries was −0.20±0.28 mm, the average absolute difference was 1.19±0.14 mm, the average maximum difference was 7.01±1.04 mm, and the average volume difference was 7.16%±3.45%. Variability in manual and algorithm segmentation was also assessed: Visual representations of local variability were generated by mapping variability on the segmentation mesh. The mean variability in manual segmentation was 0.98 mm and in algorithm segmentation was 0.63 mm and the differences of about 51.5% of the points comprising the average algorithm boundary are insignificant ( P ⩽ 0.01 ) to the manual average boundary.