59 research outputs found
Generic Feature Learning for Wireless Capsule Endoscopy Analysis
The interpretation and analysis of wireless capsule endoscopy (WCE) recordings is a complex task which requires sophisticated computer aided decision (CAD) systems to help physicians with video screening and, finally, with the diagnosis. Most CAD systems used in capsule endoscopy share a common system design, but use very different image and video representations. As a result, each time a new clinical application of WCE appears, a new CAD system has to be designed from the scratch. This makes the design of new CAD systems very time consuming. Therefore, in this paper we introduce a system for small intestine motility characterization, based on Deep Convolutional Neural Networks, which circumvents the laborious step of designing specific features for individual motility events. Experimental results show the superiority of the learned features over alternative classifiers constructed using state-of-the-art handcrafted features. In particular, it reaches a mean classification accuracy of 96% for six intestinal motility events, outperforming the other classifiers by a large margin (a 14% relative performance increase)
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
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Machine learning based small bowel video capsule endoscopy analysis: Challenges and opportunities
YesVideo capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of gastric disorders. However, owing to the high redundancy and subtle manifestation of anomalies among thousands of frames, the manual construal of VCE videos requires considerable patience, focus, and time. The automatic analysis of these videos using computational methods is a challenge as the capsule is untamed in motion and captures frames inaptly. Several machine learning (ML) methods, including recent deep convolutional neural networks approaches, have been adopted after evaluating their potential of improving the VCE analysis. However, the clinical impact of these methods is yet to be investigated. This survey aimed to highlight the gaps between existing ML-based research methodologies and clinically significant rules recently established by gastroenterologists based on VCE. A framework for interpreting raw frames into contextually relevant frame-level findings and subsequently merging these findings with meta-data to obtain a disease-level diagnosis was formulated. Frame-level findings can be more intelligible for discriminative learning when organized in a taxonomical hierarchy. The proposed taxonomical hierarchy, which is formulated based on pathological and visual similarities, may yield better classification metrics by setting inference classes at a higher level than training classes. Mapping from the frame level to the disease level was structured in the form of a graph based on clinical relevance inspired by the recent international consensus developed by domain experts. Furthermore, existing methods for VCE summarization, classification, segmentation, detection, and localization were critically evaluated and compared based on aspects deemed significant by clinicians. Numerous studies pertain to single anomaly detection instead of a pragmatic approach in a clinical setting. The challenges and opportunities associated with VCE analysis were delineated. A focus on maximizing the discriminative power of features corresponding to various subtle lesions and anomalies may help cope with the diverse and mimicking nature of different VCE frames. Large multicenter datasets must be created to cope with data sparsity, bias, and class imbalance. Explainability, reliability, traceability, and transparency are important for an ML-based diagnostics system in a VCE. Existing ethical and legal bindings narrow the scope of possibilities where ML can potentially be leveraged in healthcare. Despite these limitations, ML based video capsule endoscopy will revolutionize clinical practice, aiding clinicians in rapid and accurate diagnosis
Edge cross-section profile for colonoscopic object detection
Colorectal cancer is the second leading cause of cancer-related deaths, claiming close to 50,000 lives annually in the United States alone. Colonoscopy is an important screening tool that has contributed to a significant decline in colorectal cancer-related deaths. During colonoscopy, a tiny video camera at the tip of the endoscope generates a video signal of the internal mucosa of the human colon. The video data is displayed on a monitor for real-time diagnosis by the endoscopist. Despite the success of colonoscopy in lowering cancer-related deaths, a significant miss rate for detection of both large polyps and cancers is estimated around 4-12%. As a result, in recent years, many computer-aided object detection techniques have been developed with the ultimate goal to assist the endoscopist in lowering the polyp miss rate. Automatic object detection in recorded video data during colonoscopy is challenging due to the noisy nature of endoscopic images caused by camera motion, strong light reflections, the wide angle lens that cannot be automatically focused, and the location and appearance variations of objects within the colon. The unique characteristics of colonoscopy video require new image/video analysis techniques.
The dissertation presents our investigation on edge cross-section profile (ECSP), a local appearance model, for colonoscopic object detection. We propose several methods to derive new features on ECSP from its surrounding region pixels, its first-order derivative profile, and its second-order derivative profile. These ECSP features describe discriminative patterns for different types of objects in colonoscopy. The new algorithms and software using the ECSP features can effectively detect three representative types of objects and extract their corresponding semantic unit in terms of both accuracy and analysis time.
The main contributions of dissertation are summarized as follows. The dissertation presents 1) a new ECSP calculation method and feature-based ECSP method that extracts features on ECSP for object detection, 2) edgeless ECSP method that calculates ECSP without using edges, 3) part-based multi-derivative ECSP algorithm that segments ECSP, its 1st - order and its 2nd - order derivative functions into parts and models each part using the method that is suitable to that part, 4) ECSP based algorithms for detecting three representative types of colonoscopic objects including appendiceal orifices, endoscopes during retroflexion operations, and polyps and extracting videos or segmented shots containing these objects as semantic units, and 5) a software package that implements these techniques and provides meaningful visual feedback of the detected results to the endoscopist. Ideally, we would like the software to provide feedback to the endoscopist before the next video frame becomes available and to process video data at the rate in which the data are captured (typically at about 30 frames per second (fps)). This real-time requirement is difficult to achieve using today\u27s affordable off-the-shelf workstations. We aim for achieving near real-time performance where the analysis and feedback complete at the rate of at least 1 fps.
The dissertation has the following broad impacts. Firstly, the performance study shows that our proposed ECSP based techniques are promising both in terms of the detection rate and execution time for detecting the appearance of the three aforementioned types of objects in colonoscopy video. Our ECSP based techniques can be extended to both detect other types of colonoscopic objects such as diverticula, lumen and vessel, and analyze other endoscopy procedures, such as laparoscopy, upper gastrointestinal endoscopy, wireless capsule endoscopy and EGD. Secondly, to our best knowledge, our polyp detection system is the only computer-aided system that can warn the endoscopist the appearance of polyps in near real time. Our retroflexion detection system is also the first computer-aided system that can detect retroflexion in near real-time. Retroflexion is a maneuver used by the endoscopist to inspect the colon area that is hard to reach. The use of our system in future clinical trials may contribute to the decline in the polyp miss rate during live colonoscopy. Our system may be used as a training platform for novice endoscopists. Lastly, the automatic documentation of detected semantic units of colonoscopic objects can be helpful to discover unknown patterns of colorectal cancers or new diseases and used as educational resources for endoscopic research
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