Multi-Contrast Computed Tomography Atlas of Healthy Pancreas
Yinchi Zhou, Ho Hin Lee, Yucheng Tang, Xin Yu, Qi Yang, Shunxing Bao,, Jeffrey M. Spraggins, Yuankai Huo, and Bennett A. Landman

TL;DR
This paper presents a high-resolution CT atlas of the pancreas across multiple contrasts, utilizing deep learning and hierarchical registration to improve anatomical alignment and facilitate population-wide pancreatic analysis.
Contribution
It introduces a novel deep learning-based preprocessing and registration pipeline for creating a detailed pancreas atlas from multi-contrast CT scans.
Findings
Achieved the best registration performance with DEEDs affine and non-rigid registration.
External evaluation yielded a 0.504 Dice score for pancreas label transfer.
Qualitative mapping clearly delineates pancreas boundaries across contrast phases.
Abstract
With the substantial diversity in population demographics, such as differences in age and body composition, the volumetric morphology of pancreas varies greatly, resulting in distinctive variations in shape and appearance. Such variations increase the difficulty at generalizing population-wide pancreas features. A volumetric spatial reference is needed to adapt the morphological variability for organ-specific analysis. Here, we proposed a high-resolution computed tomography (CT) atlas framework specifically optimized for the pancreas organ across multi-contrast CT. We introduce a deep learning-based pre-processing technique to extract the abdominal region of interests (ROIs) and leverage a hierarchical registration pipeline to align the pancreas anatomy across populations. Briefly, DEEDs affine and non-rigid registration are performed to transfer patient abdominal volumes to a fixed…
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Taxonomy
TopicsPancreatic and Hepatic Oncology Research · Medical Image Segmentation Techniques · Advanced Neural Network Applications
MethodsALIGN
