Examining the effect of synthetic data augmentation in polyp detection and segmentation

Adjei, Prince Ebenezer; Lonseko, Zenebe Markos; Du, Wenju; Zhang, Han; Rao, Nini

doi:10.1007/s11548-022-02651-x

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Additionally, studies should preferably only use the results from expert CCE readers to test the performance of their AI methods, since the concluded sensitivities and specificities represent the ability of the AI models to reach the same performance levels as these readers. Besides CNN, which requires an adequate number of coloscopy images, synthetic samples can also be used as artificial intelligence methods [ 24 , 25 ]. Finally, when these gaps and barriers have been overcome, prospective clinical trials have to confirm the accuracy of the optimized CNN models.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Colon Capsule Endoscopy—A Systematic Review

et al. 2022

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Background and aims: The applicability of colon capsule endoscopy in daily practice is limited by the accompanying labor-intensive reviewing time and the risk of inter-observer variability. Automated reviewing of colon capsule endoscopy images using artificial intelligence could be timesaving while providing an objective and reproducible outcome. This systematic review aims to provide an overview of the available literature on artificial intelligence for reviewing colonic mucosa by colon capsule endoscopy and to assess the necessary action points for its use in clinical practice. Methods: A systematic literature search of literature published up to January 2022 was conducted using Embase, Web of Science, OVID MEDLINE and Cochrane CENTRAL. Studies reporting on the use of artificial intelligence to review second-generation colon capsule endoscopy colonic images were included. Results: 1017 studies were evaluated for eligibility, of which nine were included. Two studies reported on computed bowel cleansing assessment, five studies reported on computed polyp or colorectal neoplasia detection and two studies reported on other implications. Overall, the sensitivity of the proposed artificial intelligence models were 86.5–95.5% for bowel cleansing and 47.4–98.1% for the detection of polyps and colorectal neoplasia. Two studies performed per-lesion analysis, in addition to per-frame analysis, which improved the sensitivity of polyp or colorectal neoplasia detection to 81.3–98.1%. By applying a convolutional neural network, the highest sensitivity of 98.1% for polyp detection was found. Conclusion: The use of artificial intelligence for reviewing second-generation colon capsule endoscopy images is promising. The highest sensitivity of 98.1% for polyp detection was achieved by deep learning with a convolutional neural network. Convolutional neural network algorithms should be optimized and tested with more data, possibly requiring the set-up of a large international colon capsule endoscopy database. Finally, the accuracy of the optimized convolutional neural network models need to be confirmed in a prospective setting.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Colon Capsule Endoscopy—A Systematic Review

et al. 2022

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Semantic Polyp Generation for Improving Polyp Segmentation Performance

Song,

Shin

2024

J. Med. Biol. Eng.

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Purpose To improve the performance of deep-learning-based image segmentation, a sufficient amount of training data is required. However, it is more difficult to obtain training images and segmentation masks for medical images than for general images. In deep-learning-based colon polyp detection and segmentation, research has recently been conducted to improve performance by generating polyp images using a generative model, and then adding them to training data. Methods We propose SemanticPolypGAN for generating colonoscopic polyp images. The proposed model can generate images using only the polyp and corresponding mask images without additional preparation of input condition. In addition, the semantic generation of the shape and texture of polyps and non-polyp parts is possible. We experimentally compare the performance of various polyp-segmentation models by integrating the generated images and masks into the training data. Results The experimental results show improved overall performance for all models and previous work. Conclusion This study demonstrates that using polyp images generated by SemanticPolypGAN as additional training data can improve polyp segmentation performance. Unlike existing methods, SemanticPolypGAN can independently control polyp and non-polyp parts in a generation.

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