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 Bennett A. Landman
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
high-resolution atlas template. To generate and evaluate the pancreas atlas
template, multi-contrast modality CT scans of 443 subjects (without reported
history of pancreatic disease, age: 15-50 years old) are processed. Comparing
with different registration state-of-the-art tools, the combination of DEEDs
affine and non-rigid registration achieves the best performance for the
pancreas label transfer across all contrast phases. We further perform external
evaluation with another research cohort of 100 de-identified portal venous
scans with 13 organs labeled, having the best label transfer performance of
0.504 Dice score in unsupervised setting. The qualitative representation (e.g.,
average mapping) of each phase creates a clear boundary of pancreas and its
distinctive contrast appearance. The deformation surface renderings across
scales (e.g., small to large volume) further illustrate the generalizability of
the proposed atlas template.