Automatic corpus callosum segmentation for standardized MR brain scanning

Qing Xu; Hong Chen; Li Zhang; Carol L. Novak

doi:10.1117/12.710090

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7 March 2007 Automatic corpus callosum segmentation for standardized MR brain scanning

Qing Xu, Hong Chen, Li Zhang, Carol L. Novak

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Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65123K (2007) https://doi.org/10.1117/12.710090
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Magnetic Resonance (MR) brain scanning is often planned manually with the goal of aligning the imaging plane with key anatomic landmarks. The planning is time-consuming and subject to inter- and intra- operator variability. An automatic and standardized planning of brain scans is highly useful for clinical applications, and for maximum utility should work on patients of all ages. In this study, we propose a method for fully automatic planning that utilizes the landmarks from two orthogonal images to define the geometry of the third scanning plane. The corpus callosum (CC) is segmented in sagittal images by an active shape model (ASM), and the result is further improved by weighting the boundary movement with confidence scores and incorporating region based refinement. Based on the extracted contour of the CC, several important landmarks are located and then combined with landmarks from the coronal or transverse plane to define the geometry of the third plane. Our automatic method is tested on 54 MR images from 24 patients and 3 healthy volunteers, with ages ranging from 4 months to 70 years old. The average accuracy with respect to two manually labeled points on the CC is 3.54 mm and 4.19 mm, and differed by an average of 2.48 degrees from the orientation of the line connecting them, demonstrating that our method is sufficiently accurate for clinical use.

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Qing Xu, Hong Chen, Li Zhang, and Carol L. Novak "Automatic corpus callosum segmentation for standardized MR brain scanning", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123K (7 March 2007); https://doi.org/10.1117/12.710090

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