WORD: Revisiting Organs Segmentation in the Whole Abdominal Region.
Year: 2021
Type: preprint
Abstract: Whole abdominal organs segmentation plays an important role in abdomen lesiondiagnosis, radiotherapy planning, and follow-up. However, delineating all
abdominal organs by oncologists manually is time-consuming and very expensive.
Recently, deep learning-based medical image segmentation has shown the
potential to reduce manual delineation efforts, but it still requires a
large-scale fine annotated dataset for training. Although many efforts in this
task, there are still few large image datasets covering the whole abdomen
region with accurate and detailed annotations for the whole abdominal organ
segmentation. In this work, we establish a large-scale \textit{W}hole abdominal
\textit{OR}gans \textit{D}ataset (\textit{WORD}) for algorithms research and
clinical applications development. This dataset contains 150 abdominal CT
volumes (30495 slices) and each volume has 16 organs with fine pixel-level
annotations and scribble-based sparse annotation, which may be the largest
dataset with whole abdominal organs annotation. Several state-of-the-art
segmentation methods are evaluated on this dataset. And, we also invited
clinical oncologists to revise the model predictions to measure the gap between
the deep learning method and real oncologists. We further introduce and
evaluate a new scribble-based weakly supervised segmentation on this dataset.
The work provided a new benchmark for the abdominal multi-organ segmentation
task and these experiments can serve as the baseline for future research and
clinical application development. The codebase and dataset will be released at:
https://github.com/HiLab-git/WORD
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3211219022