3 research outputs found
Application of Structural Similarity Analysis of Visually Salient Areas and Hierarchical Clustering in the Screening of Similar Wireless Capsule Endoscopic Images
Small intestinal capsule endoscopy is the mainstream method for inspecting
small intestinal lesions,but a single small intestinal capsule endoscopy will
produce 60,000 - 120,000 images, the majority of which are similar and have no
diagnostic value. It takes 2 - 3 hours for doctors to identify lesions from
these images. This is time-consuming and increase the probability of
misdiagnosis and missed diagnosis since doctors are likely to experience visual
fatigue while focusing on a large number of similar images for an extended
period of time.In order to solve these problems, we proposed a similar wireless
capsule endoscope (WCE) image screening method based on structural similarity
analysis and the hierarchical clustering of visually salient sub-image blocks.
The similarity clustering of images was automatically identified by
hierarchical clustering based on the hue,saturation,value (HSV) spatial color
characteristics of the images,and the keyframe images were extracted based on
the structural similarity of the visually salient sub-image blocks, in order to
accurately identify and screen out similar small intestinal capsule endoscopic
images. Subsequently, the proposed method was applied to the capsule endoscope
imaging workstation. After screening out similar images in the complete data
gathered by the Type I OMOM Small Intestinal Capsule Endoscope from 52 cases
covering 17 common types of small intestinal lesions, we obtained a lesion
recall of 100% and an average similar image reduction ratio of 76%. With
similar images screened out, the average play time of the OMOM image
workstation was 18 minutes, which greatly reduced the time spent by doctors
viewing the images
Lesion2Vec: Deep Metric Learning for Few-Shot Multiple Lesions Recognition in Wireless Capsule Endoscopy Video
Effective and rapid detection of lesions in the Gastrointestinal tract is
critical to gastroenterologist's response to some life-threatening diseases.
Wireless Capsule Endoscopy (WCE) has revolutionized traditional endoscopy
procedure by allowing gastroenterologists visualize the entire GI tract
non-invasively. Once the tiny capsule is swallowed, it sequentially capture
images of the GI tract at about 2 to 6 frames per second (fps). A single video
can last up to 8 hours producing between 30,000 to 100,000 images. Automating
the detection of frames containing specific lesion in WCE video would relieve
gastroenterologists the arduous task of reviewing the entire video before
making diagnosis. While the WCE produces large volume of images, only about 5\%
of the frames contain lesions that aid the diagnosis process. Convolutional
Neural Network (CNN) based models have been very successful in various image
classification tasks. However, they suffer excessive parameters, are sample
inefficient and rely on very large amount of training data. Deploying a CNN
classifier for lesion detection task will require time-to-time fine-tuning to
generalize to any unforeseen category. In this paper, we propose a metric-based
learning framework followed by a few-shot lesion recognition in WCE data.
Metric-based learning is a meta-learning framework designed to establish
similarity or dissimilarity between concepts while few-shot learning (FSL) aims
to identify new concepts from only a small number of examples. We train a
feature extractor to learn a representation for different small bowel lesions
using metric-based learning. At the testing stage, the category of an unseen
sample is predicted from only a few support examples, thereby allowing the
model to generalize to a new category that has never been seen before. We
demonstrated the efficacy of this method on real patient capsule endoscopy
data
Survey of Computer Vision and Machine Learning in Gastrointestinal Endoscopy
This paper attempts to provide the reader a place to begin studying the
application of computer vision and machine learning to gastrointestinal (GI)
endoscopy. They have been classified into 18 categories. It should be be noted
by the reader that this is a review from pre-deep learning era. A lot of deep
learning based applications have not been covered in this thesis