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 hist...
| Published in: | Journal of Digital Imaging Vol. 36; no. 4; pp. 1608 - 1624 |
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| Main Authors: | , , , |
| Format: | pictorial research tables/charts Journal Article |
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Springer Nature
Aug2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169808809&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169808809 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2023 vid: 36 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 169808809 162871601 169808809 169808809 10.1007/s10278-023-00814-z 169808809 ppf: 1608 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Influence of Data Augmentation Strategies on the Segmentation of Oral Histological Images Using Fully Convolutional Neural Networks. aug: au: dos Santos, Dalí F. D. de Faria, Paulo R. Travençolo, Bruno A. N. do Nascimento, Marcelo Z. affil: Faculty of Computer Science, Federal University of Uberlândia, Brazil and Institute of Biomedical Science, Federal University of Uberlândia, Uberlândia, Brazil sug: subj: Mouth Neoplasms Diagnosis Carcinoma, Squamous Cell Diagnosis Image Processing, Computer Assisted Methods Neural Networks (Computer) Methods Staining and Labeling Methods Data Analysis Methods Convolutional Neural Networks Diagnosis, Laboratory Methods Human Image Enhancement Descriptive Statistics Experimental Studies Microarray Analysis Methods Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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