COBRA: Cpu-Only aBdominal oRgan segmentAtion

TL;DR

This paper introduces a lightweight, CPU-only 3D CNN for abdominal organ segmentation that achieves high accuracy and fast inference, making automated segmentation more accessible without specialized hardware.

Contribution

A small, efficient 3D CNN model optimized for CPU inference, enabling accurate abdominal organ segmentation without GPU dependency.

Findings

High segmentation accuracy (e.g., Liver: 97.3%)

Inference time of 1.6 seconds per image

CPU-only deployment without performance loss

Abstract

Abdominal organ segmentation is a difficult and time-consuming task. To reduce the burden on clinical experts, fully-automated methods are highly desirable. Current approaches are dominated by Convolutional Neural Networks (CNNs) however the computational requirements and the need for large data sets limit their application in practice. By implementing a small and efficient custom 3D CNN, compiling the trained model and optimizing the computational graph: our approach produces high accuracy segmentations (Dice Similarity Coefficient (%): Liver: 97.3$\pm$1.3, Kidneys: 94.8$\pm$3.6, Spleen: 96.4$\pm$3.0, Pancreas: 80.9$\pm$10.1) at a rate of 1.6 seconds per image. Crucially, we are able to perform segmentation inference solely on CPU (no GPU required), thereby facilitating easy and widespread deployment of the model without specialist hardware.