Dr. Michele Tagliati Profile

Dr. Michele Tagliati

at Cedars-Sinai Medical Ctr

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Proceedings Article | 15 March 2019 Presentation + Paper

Substantia nigra segmentation on neuromelanin-sensitive MRI

Touseef Ahmad Qureshi, Elliot Hogg, Cody Lynch, Debiao Li, Michele Tagliati, Zhaoyang Fan

Proceedings Volume 10953, 109530Y (2019) https://doi.org/10.1117/12.2512909

KEYWORDS: Image segmentation, Magnetic resonance imaging, Edge detection, Brain, Image processing, Medical research

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Efficient segmentation of the substantia nigra (SN) in midbrain cerebral images is a prerequisite for reliable quantification and evaluation of severity of Parkinson’s disease (PD). General-purpose edge-detection techniques aren’t sufficient to for accurate segmentation due to inconsistent shape and fuzzy boundaries. Additionally, the regional properties (such as grey level) of the SN and other cerebral structures are significantly similar, and thus misclassification of segmented regions is also expected. This paper presents an algorithm for localization and segmentation of the SN in neuromelanin-sensitive magnetic resonance imaging (MRI) of the midbrain. The localization is performed using a cross-correlation template matching model in which multiple templates were used to find a match with Cerebral Peduncle, a collective structure of the SN and cerebral crus in the midbrain. We adopted a new approach that uses the parametric equation of cardioid plane curve (a curve that resembles the general structure of cerebral peduncle) to generate multiple deformable templates for localization algorithm. The segmentation of the SN is then performed using the freeform active contour segmentation model in the localized region. A total of 60 slices (10 training, 50 testing), obtained from 19 scans of 10 healthy volunteers and 9 patients with PD, were acquired using a 3T MRI system. The localization algorithm succeeded in 99.8% of the cases, while the segmentation method outperformed with an average sensitivity= 0.83, specificity = 0.97, and Dice-score = 0.73.

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