1,272 research outputs found
LLCaps: Learning to Illuminate Low-Light Capsule Endoscopy with Curved Wavelet Attention and Reverse Diffusion
Wireless capsule endoscopy (WCE) is a painless and non-invasive diagnostic
tool for gastrointestinal (GI) diseases. However, due to GI anatomical
constraints and hardware manufacturing limitations, WCE vision signals may
suffer from insufficient illumination, leading to a complicated screening and
examination procedure. Deep learning-based low-light image enhancement (LLIE)
in the medical field gradually attracts researchers. Given the exuberant
development of the denoising diffusion probabilistic model (DDPM) in computer
vision, we introduce a WCE LLIE framework based on the multi-scale
convolutional neural network (CNN) and reverse diffusion process. The
multi-scale design allows models to preserve high-resolution representation and
context information from low-resolution, while the curved wavelet attention
(CWA) block is proposed for high-frequency and local feature learning.
Furthermore, we combine the reverse diffusion procedure to further optimize the
shallow output and generate the most realistic image. The proposed method is
compared with ten state-of-the-art (SOTA) LLIE methods and significantly
outperforms quantitatively and qualitatively. The superior performance on GI
disease segmentation further demonstrates the clinical potential of our
proposed model. Our code is publicly accessible.Comment: To appear in MICCAI 2023. Code availability:
https://github.com/longbai1006/LLCap
Technical Evolution of Medical Endoscopy
This paper gives a summary of the technical evolution of medical endoscopy. The first documented redirection of sunlight into the human body dates back to the 16th century. Rigid tubes with candle light were given a trial later on. Low light intensity forced the development of alternative light sources. Some of these experiments included burning chemical components. Electric lighting finally solved the problems of heat production and smoke. Flexible endoscopy increased the range of medical examinations as it allowed access to tight and angular body cavities. The first cameras for endoscopic applications made taking photos from inside the human body possible. Later on, digital video endoscopy made endoscopes easier to use and allowed multiple spectators to observe the endoscopic intervention. Swallowable capsules called pill-cams made endoscopic examinations of the small intestine possible. Modern technologies like narrow band imaging and fluorescence endoscopy increased the diagnostic significance of endoscopic images. Today, image processing is applied to decrease noise and enhance image quality. These enhancements have made medical endoscopy an invaluable tool in many diagnostic processes. In closing, an example is given of an interdisciplinary examination, which is taken from the archaeological field.
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
Wireless capsule gastrointestinal endoscopy: direction of arrival estimation based localization survey
One of the significant challenges in Capsule Endoscopy (CE) is to precisely determine the pathologies location. The localization process is primarily estimated using the received signal strength from sensors in the capsule system through its movement in the gastrointestinal (GI) tract. Consequently, the wireless capsule endoscope (WCE) system requires improvement to handle the lack of the capsule instantaneous localization information and to solve the relatively low transmission data rate challenges. Furthermore, the association between the capsule’s transmitter position, capsule location, signal reduction and the capsule direction should be assessed. These measurements deliver significant information for the instantaneous capsule localization systems based on TOA (time of arrival) approach, PDOA (phase difference of arrival), RSS (received signal strength), electromagnetic, DOA (direction of arrival) and video tracking approaches are developed to locate the WCE precisely. The current article introduces the acquisition concept of the GI medical images using the endoscopy with a comprehensive description of the endoscopy system components. Capsule localization and tracking are considered to be the most important features of the WCE system, thus the current article emphasizes the most common localization systems generally, highlighting the DOA-based localization systems and discusses the required significant research challenges to be addressed
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