Unravelling the effect of data augmentation transformations in polyp segmentation
- PDF / 930,445 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 29 Downloads / 185 Views
ORIGINAL ARTICLE
Unravelling the effect of data augmentation transformations in polyp segmentation Luisa F. Sánchez-Peralta1
· Artzai Picón2
· Francisco M. Sánchez-Margallo1
· J. Blas Pagador1
Received: 13 January 2020 / Accepted: 14 September 2020 © The Author(s) 2020
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 CVCEndoSceneStill 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. Keywords Polyp segmentation · Deep learning · Data augmentation · Transformations · Semantic segmentation
Introduction Deep learning techniques have been widely used for the last years as they have proved their ability to extract features for different computer vision tasks such as object detection, classification or segmentation [1]. Undoubtedly, these techniques have also been used for medical imaging with great Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11548-020-02262-4) contains supplementary material, which is available to authorized users.
B
Luisa F. Sánchez-Peralta [email protected]
1
Jesús Usón Minimally Invasive Surgery Centre, Road N-521, km 41.8, 10071 Cáceres, Spain
2
Tecnalia Research and Innovation, Zamudio, Spain
success [2, 3]. Even though, one limitation that must be faced in this field is the lack of large datasets with relevant annotatio
Data Loading...
