Automatic recognition of midline shift on brain CT images

Authors:
Chun-Chih Liao;Furen Xiao;Jau-Min Wong;I-Jen Chiang
Affiliations:
Graduate Institute of Biomedical Engineering, National Taiwan University, 1 Jen Ai Road, Sec 1, Taipei, Taiwan and Taipei Hospital, Department of Health, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, 1 Jen Ai Road, Sec 1, Taipei, Taiwan and National Taiwan University Hospital, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, 1 Jen Ai Road, Sec 1, Taipei, Taiwan and National Taiwan University Hospital, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, 1 Jen Ai Road, Sec 1, Taipei, Taiwan and Graduate Institute of Medical Informatics, Taipei Medical University, 250 Wu-Xin ...
Venue:
Computers in Biology and Medicine
Year:
2010

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Abstract

Midline shift is one of the most important quantitative features clinicians use to evaluate the severity of brain compression by various pathologies. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain and the cerebrospinal fluid spaces. This paper proposes a novel method to identify the deformed midline according to the above hypothesis. In this model, the deformed midline is decomposed into three segments: the upper and the lower straight segments representing parts of the tough dura mater separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline pixels, to simulate maximal bilateral symmetry. A genetic algorithm is applied to derive the optimal values of the control points of the Bezier curve. Our algorithm was evaluated on pathological images from 81 consecutive patients treated in a single institute over a period of one year. Our algorithm is able to recognize the deformed midlines in 65 (80%) of the patients with an accuracy of 95%, making it a useful tool for clinical decision-making.