  Image segmentation is an important task in medical imaging. It constitutes
the backbone of a wide variety of clinical diagnostic methods, treatments, and
computer-aided surgeries. In this paper, we propose a multi-kernel image
segmentation net (MKIS-Net), which uses multiple kernels to create an efficient
receptive field and enhance segmentation performance. As a result of its
multi-kernel design, MKIS-Net is a light-weight architecture with a small
number of trainable parameters. Moreover, these multi-kernel receptive fields
also contribute to better segmentation results. We demonstrate the efficacy of
MKIS-Net on several tasks including segmentation of retinal vessels, skin
lesion segmentation, and chest X-ray segmentation. The performance of the
proposed network is quite competitive, and often superior, in comparison to
state-of-the-art methods. Moreover, in some cases MKIS-Net has more than an
order of magnitude fewer trainable parameters than existing medical image
segmentation alternatives and is at least four times smaller than other
light-weight architectures.
