Ms. Delaram Shakoor Profile

Delaram Shakoor

at Johns Hopkins Hospital

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Publications (1)

Proceedings Article | 15 March 2019 Presentation + Paper

Coupled active shape models for automated segmentation and landmark localization in high-resolution CT of the foot and ankle

M. Brehler, A. Islam, L. Vogelsang, D. Yang, W. Sehnert, D. Shakoor, S. Demehri, J. Siewerdsen, W. Zbijewski

Proceedings Volume 10953, 109530P (2019) https://doi.org/10.1117/12.2515022

KEYWORDS: Bone, Image segmentation, 3D modeling, Shape analysis, Data modeling, Diagnostics, 3D image processing, Biomedical engineering

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Purpose: We develop an Active Shape Model (ASM) framework for automated bone segmentation and anatomical landmark localization in weight-bearing Cone-Beam CT (CBCT). To achieve a robust shape model fit in narrow joint spaces of the foot (0.5 – 1 mm), a new approach for incorporating proximity constraints in ASM (coupled ASM, cASM) is proposed. Methods: In cASM, shape models of multiple adjacent foot bones are jointly fit to the CBCT volume. This coupling enables checking for proximity between the evolving shapes to avoid situations where a conventional single-bone ASM might erroneously fit to articular surfaces of neighbouring bones. We used 21 extremity CBCT scans of the weight-bearing foot to compare segmentation and landmark localization accuracy of ASM and cASM in leave-one-out validation. Each scan was used as a test image once; shape models of calcaneus, talus, navicular, and cuboid were built from manual surface segmentations of the remaining 20 scans. The models were augmented with seven anatomical landmarks used for common measurements of foot alignment. The landmarks were identified in the original CBCT volumes and mapped onto mean bone shape surfaces. ASM and cASM were run for 100 iterations, and the number of principal shape components was increased every 10 iterations. Automated landmark localization was achieved by applying known point correspondences between landmark vertices on the mean shape and vertices of the final active shape segmentation of the test image. Results: Root Mean Squared (RMS) error of bone surface segmentation improved from 3.6 mm with conventional ASM to 2.7 mm with cASM. Furthermore, cASM achieved convergence (no change in RMS error with iteration) after ~40 iterations of shape fitting, compared to ~60 iterations for ASM. Distance error in landmark localization was 25% to 55% lower (depending on the landmark) with cASM than with ASM. The importance of using a coupled model is underscored by the finding that cASM detected and corrected collisions between evolving shapes in 50% to 80% (depending on the bone) of shape model fits. Conclusion: The proposed cASM framework improves accuracy of shape model fits, especially in complexes of tightly interlocking, articulated joints. The approach enables automated anatomical analysis in volumetric imaging of the foot and ankle, where narrow joint spaces challenge conventional shape models.

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