A geometric method for the detection and correction of segmentation leaks of anatomical structures in volumetric medical images

• Published in: International journal for computer assisted radiology and surgery Vol. 11; no. 3; pp. 369 - 380
• Main Authors: Kronman, Achia, Joskowicz, Leo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2016
• Subjects: Algorithms; Computer Imaging; Computer Science; Health Informatics; Humans; Imaging; Imaging, Three-Dimensional - methods; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - physiopathology; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Radiology; Reproducibility of Results; Surgery; Tomography, X-Ray Computed - standards; Vision; Laplace deformation; Volumetric images; Segmentation errors correction; Medical image segmentation
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-015-1285-z

Purpose Patient-specific models of anatomical structures and pathologies generated from volumetric medical images play an increasingly central role in many aspects of patient care. A key task in generating these models is the segmentation of anatomical structures and pathologies of interest. Although numerous segmentation methods are available, they often produce erroneous delineations that require time-consuming modifications. Methods    We present a new geometry-based algorithm for the reliable detection and correction of segmentation errors in volumetric medical images. The method is applicable to anatomical structures consisting of a few 3D star-shaped components. First, it detects segmentation errors by casting rays from the initial segmentation interior to its outer surface. It then classifies the segmentation surface into correct and erroneous regions by minimizing an energy functional that incorporates first- and second-order properties of the rays lengths. Finally, it corrects the segmentation errors by computing new locations for the erroneous surface points by Laplace deformation so that the new surface has maximum smoothness with respect to the rays-length gradient magnitude. Results    Our evaluation on initial segmentations of 16 abdominal aortic aneurysm and 12 lung tumors in CT scans obtained by both adaptive region-growing and active contours level-set segmentation improved the volumetric overlap error by 66 and 70.5 % respectively, with respect to the ground-truth. Conclusions    The advantages of our method are that it is independent of the initial segmentation algorithm that covers a variety of anatomical structures and pathologies, that it does not require a shape prior, and that it requires minimal user interaction.