Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
- URL: http://arxiv.org/abs/2110.01939v1
- Date: Tue, 5 Oct 2021 11:07:42 GMT
- Title: Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation
- Authors: Adrian Galdran, Gustavo Carneiro, Miguel A. Gonz\'alez Ballester
- Abstract summary: 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.
Double encoder-decoder networks clearly outperform their single encoder-decoder counterparts in all cases.
- Score: 19.338350044289736
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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