Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements

Franz Thaler, Christian Payer, Horst Bischof, Darko Stern

Research output: Contribution to conferencePaper

Abstract

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
Original languageEnglish
Number of pages8
Publication statusPublished - 2021
Event24th International Conference on Medical Image Computing and Computer Assisted Intervention: MICCAI 2021 - Virtual, Strasbourg, France
Duration: 27 Sep 20211 Oct 2021

Conference

Conference24th International Conference on Medical Image Computing and Computer Assisted Intervention
Abbreviated titleMICCAI 2021
Country/TerritoryFrance
CityStrasbourg
Period27/09/211/10/21

Fingerprint

Dive into the research topics of 'Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements'. Together they form a unique fingerprint.

Cite this