Automatic lesion detection in wireless capsule endoscopy — A simple solution for a complex problem

2014 ◽  
Author(s):  
Dimitris K. Iakovidis ◽  
Anastasios Koulaouzidis
Keyword(s):  
Capsule Endoscopy ◽  
Complex Problem ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Simple Solution ◽  
Wireless Capsule

scholarly journals Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-Art

2020 ◽  
Vol 16 (9) ◽  
pp. 1074-1084
Author(s):  
Amira S. Ashour ◽  
Nilanjan Dey ◽  
Waleed S. Mohamed ◽  
Jolanda G. Tromp ◽  
R. Simon Sherratt ◽  
...  
Keyword(s):  
Video Analysis ◽  
Capsule Endoscopy ◽  
Feature Learning ◽  
Wireless Capsule Endoscopy ◽  
Image Resolution ◽  
Lesion Detection ◽  
Gi Tract ◽  
Video Information ◽  
Speed Up ◽  
Wireless Capsule

Wireless Capsule Endoscopy (WCE) is a highly promising technology for gastrointestinal (GI) tract abnormality diagnosis. However, low image resolution and low frame rates are challenging issues in WCE. In addition, the relevant frames containing the features of interest for accurate diagnosis only constitute 1% of the complete video information. For these reasons, analyzing the WCE videos is still a time consuming and laborious examination for the gastroenterologists, which reduces WCE system usability. This leads to the emergent need to speed-up and automates the WCE video process for GI tract examinations. Consequently, the present work introduced the concept of WCE technology, including the structure of WCE systems, with a focus on the medical endoscopy video capturing process using image sensors. It discussed also the significant characteristics of the different GI tract for effective feature extraction. Furthermore, video approaches for bleeding and lesion detection in the WCE video were reported with computer-aided diagnosis systems in different applications to support the gastroenterologist in the WCE video analysis. In image enhancement, WCE video review time reduction is also discussed, while reporting the challenges and future perspectives, including the new trend to employ the deep learning models for feature Learning, polyp recognition, and classification, as a new opportunity for researchers to develop future WCE video analysis techniques.

scholarly journals Prelimenary results of red lesion segmentation in WCE images

2021 ◽  
Vol 297 ◽  
pp. 01060
Author(s):  
Said Charfi ◽  
Mohamed El Ansari ◽  
Ayoub Ellahyani ◽  
Ilyas El Jaafari
Keyword(s):  
Deep Learning ◽  
Capsule Endoscopy ◽  
Imaging Technique ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Gastrointestinal Illness ◽  
Lesion Segmentation ◽  
Entire Small Bowel ◽  
Wireless Capsule

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.

scholarly journals Multi-scale Features Fusion for the Detection of Tiny Bleeding in Wireless Capsule Endoscopy Images

2022 ◽  
Vol 3 (1) ◽  
pp. 1-19
Author(s):  
Feng Lu ◽  
Wei Li ◽  
Song Lin ◽  
Chengwangli Peng ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Capsule Endoscopy ◽  
State Of The Art ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Lesion Detection ◽  
Feature Representation ◽  
Common Symptom ◽  
Content Type ◽  
Multi Scale ◽  
Wireless Capsule

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

Wireless Capsule Endoscopy – Uses Beyond GI Bleeding?

Endoscopy ◽  
2006 ◽  
Vol 38 (11) ◽  
Author(s):  
P McConville ◽  
WJ Cash ◽  
RGP Watson ◽  
JS Collins
Keyword(s):  
Capsule Endoscopy ◽  
Wireless Capsule Endoscopy ◽  
Gi Bleeding ◽  
Wireless Capsule

The Role of Wireless Capsule Endoscopy (WCE) in the Detection of Occult Primary Neuroendocrine Tumors

2017 ◽  
Vol 26 (2) ◽  
pp. 151-156
Author(s):  
Manuele Furnari ◽  
Andrea Buda ◽  
Gabriele Delconte ◽  
Davide Citterio ◽  
Theodor Voiosu ◽  
...  
Keyword(s):  
Neuroendocrine Tumor ◽  
Neuroendocrine Tumors ◽  
Capsule Endoscopy ◽  
Primary Tumor ◽  
Diagnostic Yield ◽  
Somatostatin Receptor ◽  
Tertiary Care ◽  
Wireless Capsule Endoscopy ◽  
First Line ◽  
Wireless Capsule

Background & Aims: Neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms with unclear etiology that may show functioning or non-functioning features. Primary tumor localization often requires integrated imaging. The European Neuroendocrine Tumors Society (ENETS) guidelines proposed wireless-capsule endoscopy (WCE) as a possible diagnostic tool for NETs, if intestinal origin is suspected. However, its impact on therapeutic management is debated. We aimed to evaluate the yield of WCE in detecting intestinal primary tumor in patients showing liver NET metastases when first-line investigations are inconclusive.Method: Twenty-four patients with histological diagnosis of metastatic NET from liver biopsy and no evidence of primary lesions at first-line investigations were prospectively studied in an ENETS-certified tertiary care center. Wireless-capsule endoscopy was requested before explorative laparotomy and intra-operative ultrasound. The diagnostic yield of WCE was compared to the surgical exploration.Results: Sixteen subjects underwent surgery; 11/16 had positive WCE identifying 16 bulging lesions. Mini-laparotomy found 13 NETs in 11/16 patients (9 small bowel, 3 pancreas, 1 bile ducts). Agreement between WCE and laparotomy was recorded in 9 patients (Sensitivity=75%; Specificity=37.5%; PPV=55%; NPV=60%). Correspondence assessed per-lesions produced similar results (Sensitivity=70%; Specificity=25%; PPV=44%; NPV=50%). No capsule retentions were recorded.Conclusions: Wireless-capsule endoscopy is not indicated as second-line investigation for patients with gastro-entero-pancreatic NETs. In the setting of a referral center, it might provide additional information when conventional investigations are inconclusive about the primary site.Abbreviations: DBE: double balloon enteroscopy; GEP-NET: gastro-entero-pancreatic neuroendocrine tumor; GI: gastrointestinal; ENETS: European Neuroendocrine Tumor Society; NET: neuroendocrine tumor; SSRS: somatostatin receptor scintigraphy; WCE: wireless capsule endoscopy.

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