Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
This work addresses the problem of early colorectal cancer detection through improved polyp segmentation for medical imaging, representing an incremental advancement over existing encoder-decoder methods.
The paper tackles the challenging task of automatic gastrointestinal polyp segmentation from endoscopic images by proposing a double encoder-decoder network strategy, where two networks are sequentially stacked to improve predictions, resulting in state-of-the-art performance across multiple datasets with a notable accuracy boost on unseen data.
Polyps represent an early sign of the development of Colorectal Cancer. The standard procedure for their detection consists of colonoscopic examination of the gastrointestinal tract. However, the wide range of polyp shapes and visual appearances, as well as the reduced quality of this image modality, turn their automatic identification and segmentation with computational tools into a challenging computer vision task. In this work, we present a new strategy for the delineation of gastrointestinal polyps from endoscopic images based on a direct extension of common encoder-decoder networks for semantic segmentation. In our approach, two pretrained encoder-decoder networks are sequentially stacked: the second network takes as input the concatenation of the original frame and the initial prediction generated by the first network, which acts as an attention mechanism enabling the second network to focus on interesting areas within the image, thereby improving the quality of its predictions. Quantitative evaluation carried out on several polyp segmentation databases shows that double encoder-decoder networks clearly outperform their single encoder-decoder counterparts in all cases. In addition, our best double encoder-decoder combination attains excellent segmentation accuracy and reaches state-of-the-art performance results in all the considered datasets, with a remarkable boost of accuracy on images extracted from datasets not used for training.