Abstract
Colonoscopy is considered the gold-standard investigation for colorectal cancer screening. However, the polyps miss rate in clinical practice is relatively high due to different factors. This presents an opportunity to use AI models to automatically detect and segment polyps, supporting clinicians to reduce the number of polyps missed. Inspired by the success of UNets, a popular strategy for solving medical image segmentation tasks, this article proposes a novel framework for polyp segmentation called CRF-EfficientUNet, which enhances UNet using the EfficientNet encoder, a combined asymmetric loss function, and Conditional Random Field as a Recurrent Neural Network (CRF-RNN) layer on top. A novel loss function that combines pixel-wise cross-entropy loss and asymmetric similarity loss to solve the unbalanced imaging data problem is proposed. Training the proposed network with this loss function can achieve a considerably higher Dice score and better polyp segmentation prediction. In addition, we add the CRF-RNN layer to the proposed framework to improve the quality of semantic segmentation. Experimental results on popular benchmark datasets show that CRF-EfficientUNet achieves state-of-the-art accuracy compared to existing methods. The results of the experiments, which are performed on the CVC-ClinicDB dataset for training and testing, are 95.55% Dice and 92.23% IoU. While the experimental results on cross-dataset using Kvasir-SEG as the training set, CVC-ColonDB as the test set are 85.59% Dice and 76.19% IoU. These results indicate that the proposed method has high generalization capability and learning ability, and it can be a compelling choice for practical applications with considerable data variations.
Highlights
C OLORECTAL cancer (CRC) is one of the most common causes of cancer-related death in the world for both men and women, with 576,858 deaths worldwide in 2020 [1]
We extend previous work by (i) modified the model architecture by remove the ensemble step and add a Conditional Random Fields (CRFs)-Recurrent Neural Network (RNN) layer, (ii) use EfficientNetB7 instead of EfficientNetB5 as encoder, (iii) conducted comprehensive experiments with multiple datasets, multiple experiment settings for comparison with recent SOTAs in polyp segmentation and ablation study
We show that EfficientNet family backbones significantly outperform ResNet and MobileNet in terms of Dice and interception over union (IoU) scores; EfficientNet backbones generally perform better as size increases; UNetEfficientNetB7 gives the best segmentation performance with 93.72% Dice and 88.63% IoU
Summary
C OLORECTAL cancer (CRC) is one of the most common causes of cancer-related death in the world for both men and women, with 576,858 deaths (account for 5.8% of all cancer deaths) worldwide in 2020 [1]. CRC usually arises from abnormal polyp growth inside the colon, polyps grow slowly and may take years to become cancer. The structure of polyps is distinguished from normal mucosa by color, size, and surface type. Though not all polyps lead to CRC, all CRC starts with polyps that become cancerous over time. Accurate detection, investigation, and analysis of types, patterns, and structures of polyps are important to reduce the spread of CRC. Colonoscopy is considered the primary method for colon screening and preventing polyps from becoming cancerous. Some of the rare types of polyps are visually difficult to VOLUME XX, 2021
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