Automated volumetric segmentation of the pancreas on cross-sectional imaging
is needed for diagnosis and follow-up of pancreatic diseases. While CT-based
pancreatic segmentation is more established, MRI-based segmentation methods are
understudied, largely due to a lack of publicly available datasets,
benchmarking research efforts, and domain-specific deep learning methods. In
this retrospective study, we collected a large dataset (767 scans from 499
participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series
from five centers between March 2004 and November 2022. We also collected CT
scans of 1,350 patients from publicly available sources for benchmarking
purposes. We developed a new pancreas segmentation method, called PanSegNet,
combining the strengths of nnUNet and a Transformer network with a new linear
attention module enabling volumetric computation. We tested PanSegNet's
accuracy in cross-modality (a total of 2,117 scans) and cross-center settings
with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's
kappa statistics for intra and inter-rater agreement evaluation and paired
t-tests for volume and Dice comparisons, respectively. For segmentation
accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level)
with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI.
There was a high correlation for pancreas volume prediction with R^2 of 0.91,
0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate
inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high
intra-observer agreement scores. All MRI data is made available at
this https URL Our source code is available at
this https URL

我们收集了一系列来自五个中心的大规模 MRI 扫描数据集，并开发了一种名为 PanSegNet 的新的胰腺分割方法，其在交叉模态和交叉中心设置下的 Dice 系数分别达到了 88.3%（使用 CT），85.0%（使用 T1W MRI）和 86.3%（使用 T2W MRI）。