Dr. Touseef Ahmad Qureshi Profile

Dr. Touseef Ahmad Qureshi

at Cedars Sinai Medical Ctr

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Publications (2)

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SPIE Journal Paper | 4 April 2022 Open Access

Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images

Touseef Ahmad Qureshi, Cody Lynch, Linda Azab, Yibin Xie, Srinivas Gaddam, Stephen Jacob Pandol, Debiao Li

JMI, Vol. 9, Issue 02, 024002, (April 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.2.024002

KEYWORDS: Pancreas, Image segmentation, Computed tomography, 3D modeling, 3D image processing, Data modeling, Performance modeling, Gold, Visual process modeling, Network architectures

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Purpose: Accurate segmentation of the pancreas using abdominal computed tomography (CT) scans is a prerequisite for a computer-aided diagnosis system to detect pathologies and perform quantitative assessment of pancreatic disorders. Manual outlining of the pancreas is tedious, time-consuming, and prone to subjective errors, and thus clearly not a viable solution for large datasets. Approach: We introduce a multiphase morphology-guided deep learning framework for efficient three-dimensional segmentation of the pancreas in CT images. The methodology works by localizing the pancreas using a modified visual geometry group-19 architecture, which is a 19-layer convolutional neural network model that helped reduce the region of interest for more efficient computation and removed most of the peripheral structures from consideration during the segmentation process. Subsequently, soft labels for segmentation of the pancreas in the localized region were generated using the U-net model. Finally, the model integrates the morphology prior of the pancreas to update soft labels and perform segmentation. The morphology prior is a single three-dimensional matrix, defined over the general shape and size of the pancreases from multiple CT abdominal images, that helps improve segmentation of the pancreas. Results: The system was trained and tested on the National Institutes of Health dataset (82 CT scans of the healthy pancreas). In fourfold cross-validation, the system produced an average Dice–SØrensen coefficient of 88.53% and outperformed state-of-the-art techniques. Conclusions: Localizing the pancreas assists in reducing segmentation errors and eliminating peripheral structures from consideration. Additionally, the morphology-guided model efficiently improves the overall segmentation of the pancreas.

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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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