Computer Vision in Esophageal Cancer: A Literature Review

Domingues, Inês; Sampaio, Inês Lucena; Duarte, Hugo; Abreu, Pedro Henriques

doi:10.1109/access.2019.2930891

Cited by 15 publications

(6 citation statements)

References 54 publications

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“…Step3 Input of input vector Input vector X that is input to the input layer can be expressed as (2).…”

Section: A Extract Multiple Correlation Indicators Based On Som Clusmentioning

confidence: 99%

“…Although the treatment methods and concepts of esophageal cancer have been improved gradually with the rapid development of science and the medical technology [2], the survival risk prediction of esophageal cancer still has some imperfections. Traditional statistical analysis is still widely used in the research on various pathological data of esophageal cancer patients.…”

Section: Introductionmentioning

confidence: 99%

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Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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This article provides a method based on self-organizing maps (SOM) neural network clustering and support vector machine (SVM) ensembles to predict the survival risk levels of esophageal cancer. Nine blood indexes related to patient survival are found by using SOM clustering method. Two critical thresholds for survival are found by plotting the receiver operating characteristic (ROC) curve twice, and the lifetime is divided into three risk levels. Using the SVM method, patients' risk levels are predicted and assessed. Four kernel functions of SVM are compared, and the prediction effect of RBF kernel function is better than other kernel functions. The parameters of SVM are optimized by using genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony (ABC) algorithm. Experimental results show that the prediction accuracies are improved by using optimization algorithms. After comparison, ABC-SVM has better prediction results than GA-SVM and PSO-SVM with a high prediction rate and fast running time. INDEX TERMS artificial bee colony, genetic algorithm, particle swarm optimization, self-organizing maps, support vector machine.

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“…Step3 Input of input vector Input vector X that is input to the input layer can be expressed as (2).…”

Section: A Extract Multiple Correlation Indicators Based On Som Clusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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“…only publicly available dataset by MICCAI'15 [29]) regions from endoscopic images. More details about other methods and using different examination modalities are discussed in [9], [30], [31].…”

Section: Related Workmentioning

confidence: 99%

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos

Ghatwary

Zolgharni

Janan

et al. 2021

IEEE J. Biomed. Health Inform.

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Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early cancerous) can improve the survival rate of the patients. Recent deep learning-based methods for selected types of esophageal abnormality detection from endoscopic images have been proposed. However, no methods have been introduced in the literature to cover the detection from endoscopic videos, detection from challenging frames and detection of more than one esophageal abnormality type. In this paper, we present an efficient method to automatically detect different types of esophageal abnormalities from endoscopic videos. We propose a novel 3D Sequential DenseConvLstm network that extracts spatiotemporal features from the input video. Our network incorporates 3D Convolutional Neural Network (3DCNN) and Convolutional Lstm (ConvLstm) to efficiently learn short and long term spatiotemporal features. The generated feature map is utilized by a region proposal network and ROI pooling layer to produce a bounding box that detects abnormality regions in each frame throughout the video. Finally, we investigate a post-processing method named Frame Search Conditional Random Field (FS-CRF) that improves the overall performance of the model by recovering the missing regions in neighborhood frames within the same clip. We extensively validate our model on an endoscopic video dataset that includes a variety of esophageal abnormalities. Our model achieved high performance using different evaluation metrics showing 93.7% recall, 92.7% precision, and 93.2% F-measure. Moreover, as no results have been reported in the literature for the esophageal abnormality detection from endoscopic videos, to validate the robustness of our model, we have tested the model on a publicly available colonoscopy video dataset, achieving the polyp detection performance in a recall of 81.18%, precision of 96.45% and F-measure 88.16%, compared to the state-of-the-art results of 78.84% recall, 90.51% precision and 84.27% F-measure using the same dataset. This demonstrates that the proposed method can be adapted to different gastrointestinal endoscopic video applications with a promising performance.

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“…However, due to individual lesion variations in shape, color, and texture, depending on the clinician experience for esophageal lesion detection still exists potential misjudgments and timeconsuming problems. To overcome the aforementioned problems, automated esophageal lesion detection using computer vision methods can be used for lesion classification and segmentation (Domingues, Sampaio et al 2019).…”

Section: Introductionmentioning

confidence: 99%

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network

Wen

et al. 2021

Medical Image Analysis

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Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion status of esophageal disease and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs). The underlying method automatically integrates dual-view contextual lesion information to extract global features and local features for esophageal lesion classification of four esophageal image types (Normal, Inflammation, Barrett, and Cancer) and proposes lesion-specific segmentation network for automatic esophageal lesion annotation of three esophageal lesion types at pixel level. For established clinical large-scale database of 1051 white-light endoscopic images, ten-fold cross-validation is used in method validation. Experiment results show that the proposed framework achieves classification with sensitivity of 0.9034, specificity of 0.9718 and accuracy of 0.9628, and the segmentation with sensitivity of 0.8018, specificity of 0.9655 and accuracy of 0.9462. All of these indicate that our method enables an efficient, accurate and reliable esophageal lesion diagnosis in clinical.The main contributions of our work can be generalized as follows: 1 For the first time, proposed ELNet enables an automatically and reliably comprehensive esophageal lesions classification of four esophageal lesion types (Normal, Inflammation, Barrett, and Cancer) and lesion-specific segmentation from clinically white-light esophageal images to make suitable and repaid diagnostic schemes for clinicians. 2 A novel Dual-Stream network (DSN) is proposed for esophageal lesion classification. DSN automatically integrates dual-view contextual lesion information using two CNN streams to complementarily extract the global features from the holistic esophageal images and the local features from the lesion patches. 3 Lesion-specific esophageal lesion annotation with Segmentation Network with Classification (SNC) strategy is proposed to automatically annotate three lesion types (Inflammation, Barrett, Cancer) at pixel level to reduce the intra-class differences of esophageal lesions. 4 A clinically large-scale database esophageal database is established for esophageal lesions classification and segmentation. This database includes 1051 white-light esophageal images, which consists of endoscopic images in four different lesion types. Each image in this database has a classification label and its corresponding segmentation annotation.

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Computer Vision in Esophageal Cancer: A Literature Review

Cited by 15 publications

References 54 publications

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network

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