E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation

Deep neural networks have evolved as the leading approach in 3D medical image
segmentation due to their outstanding performance. However, the ever-increasing
model size and computation cost of deep neural networks have become the primary
barrier to deploying them on real-world resource-limited hardware. In pursuit
of improving performance and efficiency, we propose a 3D medical image
segmentation model, named Efficient to Efficient Network (E2ENet),
incorporating two parametrically and computationally efficient designs. i.
Dynamic sparse feature fusion (DSFF) mechanism: it adaptively learns to fuse
informative multi-scale features while reducing redundancy. ii. Restricted
depth-shift in 3D convolution: it leverages the 3D spatial information while
keeping the model and computational complexity as 2D-based methods. We conduct
extensive experiments on BTCV, AMOS-CT and Brain Tumor Segmentation Challenge,
demonstrating that E2ENet consistently achieves a superior trade-off between
accuracy and efficiency than prior arts across various resource constraints.
E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while
saving over 68\% parameter count and 29\% FLOPs in the inference phase,
compared with the previous best-performing method. Our code has been made
available at: https://github.com/boqian333/E2ENet-Medical.