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Unravelling the effect of data augmentation transformations in polyp segmentation.

Abstract：

PURPOSE:Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning. METHODS:A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset. RESULTS:This basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance. CONCLUSION:Despite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.

journal_name

Int J Comput Assist Radiol Surg

journal_title

International journal of computer assisted radiology and surgery

authors

Sánchez-Peralta LF,Picón A,Sánchez-Margallo FM,Pagador JB

doi

10.1007/s11548-020-02262-4

subject

Has Abstract

pub_date

2020-12-01 00:00:00

pages

1975-1988

issue

12

eissn

1861-6410

issn

1861-6429

pii

10.1007/s11548-020-02262-4

journal_volume

15

pub_type

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