Focus-TransUnet3D: High-Precision Model for 3D Segmentation of Medical Point Targets

Deep learning has been extensively applied in medical image segmentation, providing significant support for disease diagnosis. However, traditional encoder-decoder networks struggle with segmenting scale-sensitive point target lesions. To address this challenge, this paper proposes an innovative incremental fusion architecture that can integrate different models and achieve significant performance improvements through complementary fusion. Based on this architecture, we developed Focus-TransUnet3D by combining the Trans-FusionNet3D model and the 3D Unet model. This model adopts a global-to-local segmentation strategy, effectively addressing the challenges of medical point target segmentation, thereby expanding the application of deep learning in the field of medical image processing. Furthermore, we design a deep fusion strategy suitable for the transformer model to adapt to multi-scale feature learning. The integration of the transformer model with convolutional neural networks brings improvements in local and global feature extraction capabilities, enhancing the applicability of our model. We evaluate our model on three clinical datasets with different target scales: the Intracranial Artery dataset, the Intracranial Aneurysm dataset, and the LiTS17 dataset. The results indicate that in the external test for intracranial aneurysm auxiliary diagnosis, the model trained with only 47 annotated samples achieved the state-of-the-art performance, attaining a Dice coefficient of 84.14% and a sensitivity of 100%. This effectively addresses the challenges of annotation scarcity and tiny targets.