  Deep learning in gastrointestinal endoscopy can assist to improve clinical
performance and be helpful to assess lesions more accurately. To this extent,
semantic segmentation methods that can perform automated real-time delineation
of a region-of-interest, e.g., boundary identification of cancer or
precancerous lesions, can benefit both diagnosis and interventions. However,
accurate and real-time segmentation of endoscopic images is extremely
challenging due to its high operator dependence and high-definition image
quality. To utilize automated methods in clinical settings, it is crucial to
design lightweight models with low latency such that they can be integrated
with low-end endoscope hardware devices. In this work, we propose NanoNet, a
novel architecture for the segmentation of video capsule endoscopy and
colonoscopy images. Our proposed architecture allows real-time performance and
has higher segmentation accuracy compared to other more complex ones. We use
video capsule endoscopy and standard colonoscopy datasets with polyps, and a
dataset consisting of endoscopy biopsies and surgical instruments, to evaluate
the effectiveness of our approach. Our experiments demonstrate the increased
performance of our architecture in terms of a trade-off between model
complexity, speed, model parameters, and metric performances. Moreover, the
resulting model size is relatively tiny, with only nearly 36,000 parameters
compared to traditional deep learning approaches having millions of parameters.
