  Vision Transformers have shown superior performance to the traditional
convolutional-based frameworks in many vision applications, including but not
limited to the segmentation of 3D medical images. To further advance this area,
this study introduces the Multi-Aperture Fusion of Transformer-Convolutional
Network (MFTC-Net), which integrates the output of Swin Transformers and their
corresponding convolutional blocks using 3D fusion blocks. The Multi-Aperture
incorporates each image patch at its original resolutions with its pyramid
representation to better preserve minute details. The proposed architecture has
demonstrated a score of 89.73 and 7.31 for Dice and HD95, respectively, on the
Synapse multi-organs dataset an improvement over the published results. The
improved performance also comes with the added benefits of the reduced
complexity of approximately 40 million parameters. Our code is available at
https://github.com/Siyavashshabani/MFTC-Net
