Multi-Stage Hierarchical Feature Extraction for Efficient 3D Medical Image Segmentation

Research has been conducted to reduce resource consumption in 3D medical image segmentation for diverse resource-constrained environments. However, decreasing the number of parameters to enhance computational efficiency can also lead to performance degradation. Moreover, these methods face challenges in balancing global and local features, increasing the risk of errors in multi-scale segmentation. This issue is particularly pronounced when segmenting small and complex structures within the human body. To address this problem, we propose a multi-stage hierarchical architecture composed of a detector and a segmentor. The detector extracts regions of interest (ROIs) in a 3D image, while the segmentor performs segmentation in the extracted ROI. Removing unnecessary areas in the detector allows the segmentation to be performed on a more compact input. The segmentor is designed with multiple stages, where each stage utilizes different input sizes. It implements a stage-skipping mechanism that deactivates certain stages using the initial input size. This approach minimizes unnecessary computations on segmenting the essential regions to reduce computational overhead. The proposed framework preserves segmentation performance while reducing resource consumption, enabling segmentation even in resource-constrained environments.