A survey on Barrett's esophagus analysis using machine learning

Souza, Luis A. de; Palm, Christoph; Mendel, Robert; Hook, Christian; Ebigbo, Alanna; Probst, Andreas; Messmann, Helmut; Weber, Silke Anna Theresa; Papa, João Paulo

doi:10.1016/j.compbiomed.2018.03.014

Cited by 51 publications

(29 citation statements)

References 52 publications

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“…These methods are discussed in [10, 11]. In this section, we will only discuss previous methods that address the detection of EAC abnormalities using the same HD-WLE images dataset that we used in our evaluation.…”

Section: Related Workmentioning

confidence: 99%

“…With the increase in the number of patients, computer-aided detection (CAD) systems have grabbed attention more frequently. There exists an amount of research available in the literature for automatic detection, segmentation and classification that employs several endoscopies such as white light endoscopy (WLE), narrow band imaging (NBI), volumetric laser endomicroscopy (VLE), confocal laser endomicroscopy (CLE) and chromoendoscopy ; these methods are summarized and discussed in [10, 11]. In the next section, an overview of the previous studies on EAC detection from HD-WLE will be discussed.…”

Section: Introductionmentioning

confidence: 99%

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Early esophageal adenocarcinoma detection using deep learning methods

Ghatwary

Zolgharni

2019

Int J CARS

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PurposeThis study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images.MethodSeveral state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG’16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested.ResultsExperimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83.ConclusionIn this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Early esophageal adenocarcinoma detection using deep learning methods

Ghatwary

Zolgharni

2019

Int J CARS

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“…Such applications are still in development and are not used in clinical practice, but computer-aided diagnosis could be useful for breast cancer [119,120]. As for PCa, AI has been used in many cancers with mixed results such as lung cancer [121], neuro-oncology [122,123], or esophagus cancer [124].…”

Section: Discussionmentioning

confidence: 99%

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

et al. 2020

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Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.

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“…There exists an amount of research available in the literature for automatic BE stage classification using different endoscopes that are discussed in Ref. 30. However, in this review, we will be mainly focusing on methods utilizing the CLE only.…”

Section: Related Work Of Be Classification Methodsmentioning

confidence: 99%

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

et al. 2019

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Barrett's esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.

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A survey on Barrett's esophagus analysis using machine learning

Cited by 51 publications

References 52 publications

Early esophageal adenocarcinoma detection using deep learning methods

Early esophageal adenocarcinoma detection using deep learning methods

The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review

In-vivo Barrett’s esophagus digital pathology stage classification through feature enhancement of confocal laser endomicroscopy

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