  Whole abdominal organ segmentation is important in diagnosing abdomen
lesions, radiotherapy, and follow-up. However, oncologists' delineating all
abdominal organs from 3D volumes is time-consuming and very expensive. 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, and there is a lack of large-scale datasets covering the
whole abdomen region with accurate and detailed annotations for the whole
abdominal organ segmentation. In this work, we establish a new large-scale
\textit{W}hole abdominal \textit{OR}gan \textit{D}ataset (\textit{WORD}) for
algorithm research and clinical application development. This dataset contains
150 abdominal CT volumes (30495 slices). Each volume has 16 organs with fine
pixel-level annotations and scribble-based sparse annotations, which may be the
largest dataset with whole abdominal organ annotation. Several state-of-the-art
segmentation methods are evaluated on this dataset. And we also invited three
experienced oncologists to revise the model predictions to measure the gap
between the deep learning method and oncologists. Afterwards, we investigate
the inference-efficient learning on the WORD, as the high-resolution image
requires large GPU memory and a long inference time in the test stage. We
further evaluate the scribble-based annotation-efficient learning on this
dataset, as the pixel-wise manual annotation is time-consuming and expensive.
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.
