Deep polyp image enhancement using region of interest with paired supervision.

Abstract

Endoscopic medical imaging in complex curved intestinal structures are prone to uneven illumination, low contrast and lack of texture information. These problems may lead to diagnostic challenges. This paper described the first supervised deep learning based image fusion framework to enable the polyp region highlight through a global image enhancement and a local region of interest (ROI) with paired supervision. Firstly, we conducted a dual attention based network in global image enhancement. The Detail Attention Maps was used to preserve more image details and the Luminance Attention Maps was used to adjust the global illumination of the image. Secondly, we adopted the advanced polyp segmentation network ACSNet to obtain the accurate mask image of lesion region in local ROI acquisition. Finally, a new image fusion strategy was proposed to realize the local enhancement effect of polyp image. Experimental results show that our method can highlight the local details of the lesion area better and reach the optimal comprehensive performance with comparing with 16 traditional and state-of-the-art enhancement algorithms. And 8 doctors and 12 medical students were asked to evaluate our method for assisting clinical diagnosis and treatment effectively. Furthermore, the first paired image dataset LHI was constructed, which will be made available as an open source to research communities.