Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks

Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling...

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Bibliographic Details
Published in:Journal of digital imaging 2023-08, Vol.36 (4), p.1608-1623
Main Authors: dos Santos, Dalí F. D., de Faria, Paulo R., Travençolo, Bruno A. N., do Nascimento, Marcelo Z.
Format: Article
Language:English
Subjects:
Artificial neural networks
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Cancer
Carcinoma, Squamous Cell - diagnostic imaging
Composition
Data augmentation
Datasets
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Imaging
Labels
Medical imaging
Medicine
Medicine & Public Health
Mouth Neoplasms - diagnostic imaging
Neural networks
Neural Networks, Computer
Oral cancer
Oral carcinoma
Oral squamous cell carcinoma
Radiology
Training
Tumors
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Summary:Segmentation of tumor regions in H &E-stained slides is an important task for a pathologist while diagnosing different types of cancer, including oral squamous cell carcinoma (OSCC). Histological image segmentation is often constrained by the availability of labeled training data since labeling histological images is a highly skilled, complex, and time-consuming task. Thus, data augmentation strategies become essential to train convolutional neural networks models to overcome the overfitting problem when only a few training samples are available. This paper proposes a new data augmentation strategy, named Random Composition Augmentation (RCAug), to train fully convolutional networks (FCN) to segment OSCC tumor regions in H &E-stained histological images. Given the input image and their corresponding label, a pipeline with a random composition of geometric, distortion, color transfer, and generative image transformations is executed on the fly. Experimental evaluations were performed using an FCN-based method to segment OSCC regions through a set of different data augmentation transformations. By using RCAug, we improved the FCN-based segmentation method from 0.51 to 0.81 of intersection-over-union (IOU) in a whole slide image dataset and from 0.65 to 0.69 of IOU in a tissue microarray images dataset.
ISSN:1618-727X
0897-1889
1618-727X
DOI:10.1007/s10278-023-00814-z