Go to MICCAI 2024 Challenge FLARE homepageLightweight Depthwise Separable U-Net for Laptop-Based Abdominal Multi-Organ Segmentation
In order to achieve efficient 3D medical image segmentation within resource-constrained environments, we have developed a U-Net-based framework. By integrating depthwise separable convolutions and efficient channel attention mechanisms, our framework is capable of achieving both efficient and accurate segmentation of various abdominal organs within a CPU-based setting. This framework not only optimizes computational efficiency but also enhances the precision of segmentation through sophisticated feature extraction, which is crucial for medical image analysis. The synergy of these techniques not only boosts the performance of our model but also suggests potential for improvement in a range of other medical image segmentation tasks. Our method achieves Dice Similarity Coefficients of 61.1%, 63.4%, and 59.4% on Asia, Europe, and North America datasets, respectively, with an average inference time of under 43 seconds per case.