  Multi-organ segmentation enables organ evaluation, accounts the relationship
between multiple organs, and facilitates accurate diagnosis and treatment
decisions. However, only few models can perform segmentation accurately because
of the lack of datasets and computational resources. On AMOS2022 challenge,
which is a large-scale, clinical, and diverse abdominal multiorgan segmentation
benchmark, we trained a 3D-UNet model with large batch and patch sizes using
multi-GPU distributed training. Segmentation performance tended to increase for
models with large batch and patch sizes compared with the baseline settings.
The accuracy was further improved by using ensemble models that were trained
with different settings. These results provide a reference for parameter
selection in organ segmentation.
