In recent years, segmentation algorithms utilizing deep learning have achieved outstanding performance in medical image segmentation. However, accurately delineating small anatomical structures continues to be a challenging task, even for the most advanced methods that produce impressive results. This challenge might arise from the use of small kernels and downsampling operations, which often emphasize complex high-level features at the expense of low-level details like edges. Inspired by recent research highlighting this challenge, we developed a novel architecture that combines the standard U Net with an additional branch harnessing the potential of large convolutional kernels. These large kernels are utilized in a decreasing-increasing manner over image features of the same size, guiding the network to focus on smaller parts. The proposed method demonstrated strong potential in segmenting small anatomical structures, surpassing our baseline and matching the performance of a robust state-of-the-art network across various datasets and domains, all while maintaining a relatively small number of parameters.
