Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements
This work addresses efficiency and generalization issues for clinical deployment of segmentation models, but it is incremental as it builds on an existing method.
The paper tackles the problem of multi-organ segmentation in medical images by modifying the SpatialConfiguration-Net architecture to reduce GPU memory usage while maintaining prediction quality, achieving efficient inference with low memory requirements.
Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice. The two main concerns are generalization to unseen data with a different visual appearance, e.g., images acquired using a different scanner, and efficiency in terms of computation time and required Graphics Processing Unit (GPU) memory. In this work, we employ a multi-organ segmentation model based on the SpatialConfiguration-Net (SCN), which integrates prior knowledge of the spatial configuration among the labelled organs to resolve spurious responses in the network outputs. Furthermore, we modified the architecture of the segmentation model to reduce its memory footprint as much as possible without drastically impacting the quality of the predictions. Lastly, we implemented a minimal inference script for which we optimized both, execution time and required GPU memory.