Dr. Deepa Anand Profile

Dr. Deepa Anand

at GE HealthCare

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Proceedings Article | 2 April 2024 Paper

Head re-orientation along desired plane using deep-learning-based landmark detection for CT images

Deepa Anand, Vanika Singhal, Maud Bonnard, Amy Deubig, Sandeep Dutta, Uday Patil, Rakesh Mullick, Bipul Das

Proceedings Volume 12926, 1292635 (2024) https://doi.org/10.1117/12.3007103

KEYWORDS: Head, Image registration, Education and training, Computed tomography, Anatomy, Image segmentation, Deep learning, Ear, 3D image processing, Pathology

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Following the acquisition of images in CT, a crucial post-processing step involves orienting the volumetric image to align with standard viewing planes, facilitating the assessment of disease extent and other pathologies. However, manual alignment is not only time-consuming but can also pose challenges in achieving consistent standard plane views, particularly for lesser skilled technologists. Existing automated solutions, primarily based on registration techniques, encounter reduced accuracy in cases involving significant rotations, pediatric patients, and instances with pronounced pathological effects. This limitation arises due to their reliance on symmetry. In severe scenarios, registration-based methods can exacerbate image misalignment compared to the original input. To address these concerns, this study introduces a landmark-based automated image alignment method. This method presents three key advantages: robust alignment across diverse data variations, the capability to identify algorithm failures and gracefully terminate, and the ability to align images with different standard planes. The effectiveness of our method is showcased through a comparative evaluation with registration-based approaches. The evaluation employs a test dataset comprising various head cases across different age groups, reaffirming the effectiveness of our proposed method.

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