62,033 research outputs found
Computer- and robot-assisted Medical Intervention
Medical robotics includes assistive devices used by the physician in order to
make his/her diagnostic or therapeutic practices easier and more efficient.
This chapter focuses on such systems. It introduces the general field of
Computer-Assisted Medical Interventions, its aims, its different components and
describes the place of robots in that context. The evolutions in terms of
general design and control paradigms in the development of medical robots are
presented and issues specific to that application domain are discussed. A view
of existing systems, on-going developments and future trends is given. A
case-study is detailed. Other types of robotic help in the medical environment
(such as for assisting a handicapped person, for rehabilitation of a patient or
for replacement of some damaged/suppressed limbs or organs) are out of the
scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00
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
Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study
Potentially precancerous polyps detected with CT colonography (CTC) need to
be removed subsequently, using an optical colonoscope (OC). Due to large
colonic deformations induced by the colonoscope, even very experienced
colonoscopists find it difficult to pinpoint the exact location of the
colonoscope tip in relation to polyps reported on CTC. This can cause unduly
prolonged OC examinations that are stressful for the patient, colonoscopist and
supporting staff.
We developed a method, based on monocular 3D reconstruction from OC images,
that automatically matches polyps observed in OC with polyps reported on prior
CTC. A matching cost is computed, using rigid point-based registration between
surface point clouds extracted from both modalities. A 3D printed and painted
phantom of a 25 cm long transverse colon segment was used to validate the
method on two medium sized polyps. Results indicate that the matching cost is
smaller at the correct corresponding polyp between OC and CTC: the value is 3.9
times higher at the incorrect polyp, comparing the correct match between polyps
to the incorrect match. Furthermore, we evaluate the matching of the
reconstructed polyp from OC with other colonic endoluminal surface structures
such as haustral folds and show that there is a minimum at the correct polyp
from CTC.
Automated matching between polyps observed at OC and prior CTC would
facilitate the biopsy or removal of true-positive pathology or exclusion of
false-positive CTC findings, and would reduce colonoscopy false-negative
(missed) polyps. Ultimately, such a method might reduce healthcare costs,
patient inconvenience and discomfort.Comment: This paper was presented at the SPIE Medical Imaging 2014 conferenc
Colon centreline calculation for CT colonography using optimised 3D opological thinning
CT colonography is an emerging technique for colorectal
cancer screening. This technique facilitates noninvasive
imaging of the colon interior by generating virtual
reality models of the colon lumen. Manual navigation
through these models is a slow and tedious process.
It is possible to automate navigation by calculating the centreline
of the colon lumen. There are numerous well documented
approaches for centreline calculation. Many of
these techniques have been developed as alternatives to 3D
topological thinning which has been discounted by others
due to its computationally intensive nature. This paper describes
a fully automated, optimised version of 3D topological
thinning that has been specifically developed for calculating
the centreline of the human colon
Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial
Background:
Intraventricular haemorrhage is a subtype of intracerebral haemorrhage, with 50% mortality and serious disability for survivors. We aimed to test whether attempting to remove intraventricular haemorrhage with alteplase versus saline irrigation improved functional outcome.
Methods:
In this randomised, double-blinded, placebo-controlled, multiregional trial (CLEAR III), participants with a routinely placed extraventricular drain, in the intensive care unit with stable, non-traumatic intracerebral haemorrhage volume less than 30 mL, intraventricular haemorrhage obstructing the 3rd or 4th ventricles, and no underlying pathology were adaptively randomly assigned (1:1), via a web-based system to receive up to 12 doses, 8 h apart of 1 mg of alteplase or 0·9% saline via the extraventricular drain. The treating physician, clinical research staff, and participants were masked to treatment assignment. CT scans were obtained every 24 h throughout dosing. The primary efficacy outcome was good functional outcome, defined as a modified Rankin Scale score (mRS) of 3 or less at 180 days per central adjudication by blinded evaluators. This study is registered with ClinicalTrials.gov, NCT00784134.
Findings:
Between Sept 18, 2009, and Jan 13, 2015, 500 patients were randomised: 249 to the alteplase group and 251 to the saline group. 180-day follow-up data were available for analysis from 246 of 249 participants in the alteplase group and 245 of 251 participants in the placebo group. The primary efficacy outcome was similar in each group (good outcome in alteplase group 48% vs saline 45%; risk ratio [RR] 1·06 [95% CI 0·88–1·28; p=0·554]). A difference of 3·5% (RR 1·08 [95% CI 0·90–1·29], p=0·420) was found after adjustment for intraventricular haemorrhage size and thalamic intracerebral haemorrhage. At 180 days, the treatment group had lower case fatality (46 [18%] vs saline 73 [29%], hazard ratio 0·60 [95% CI 0·41–0·86], p=0·006), but a greater proportion with mRS 5 (42 [17%] vs 21 [9%]; RR 1·99 [95% CI 1·22–3·26], p=0·007). Ventriculitis (17 [7%] alteplase vs 31 [12%] saline; RR 0·55 [95% CI 0·31–0·97], p=0·048) and serious adverse events (114 [46%] alteplase vs 151 [60%] saline; RR 0·76 [95% CI 0·64–0·90], p=0·002) were less frequent with alteplase treatment. Symptomatic bleeding (six [2%] in the alteplase group vs five [2%] in the saline group; RR 1·21 [95% CI 0·37–3·91], p=0·771) was similar.
Interpretation:
In patients with intraventricular haemorrhage and a routine extraventricular drain, irrigation with alteplase did not substantially improve functional outcomes at the mRS 3 cutoff compared with irrigation with saline. Protocol-based use of alteplase with extraventricular drain seems safe. Future investigation is needed to determine whether a greater frequency of complete intraventricular haemorrhage removal via alteplase produces gains in functional status
A fully automatic CAD-CTC system based on curvature analysis for standard and low-dose CT data
Computed tomography colonography (CTC) is a rapidly evolving noninvasive medical investigation that is viewed by radiologists as a potential screening technique for the detection of colorectal polyps. Due to the technical advances in CT system design, the volume of data required to be processed by radiologists has increased significantly, and as a consequence the manual analysis of this information has become an increasingly time consuming process whose results can be affected by inter- and intrauser variability. The aim of this paper is to detail the implementation of a fully integrated CAD-CTC system that is able to robustly identify the clinically significant polyps in the CT data. The CAD-CTC system described in this paper is a multistage implementation whose main system components are: 1) automatic colon segmentation; 2) candidate surface extraction; 3) feature extraction; and 4) classification. Our CAD-CTC system performs at 100% sensitivity for polyps larger than 10 mm, 92% sensitivity for polyps in the range 5 to 10 mm, and 57.14% sensitivity for polyps smaller than 5 mm with an average of 3.38 false positives per dataset. The developed system has been evaluated on synthetic and real patient CT data acquired with standard and low-dose radiation levels
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Aqueous Humor Outflow Structure and Function Imaging At the Bench and Bedside: A Review.
Anterior segment glaucoma clinical care and research has recently gained new focus because of novel imaging modalities and the advent of angle-based surgical treatments. Traditional investigation drawn to the trabecular meshwork now emphasizes the entire conventional aqueous humor outflow (AHO) pathway from the anterior chamber to the episcleral vein. AHO investigation can be divided into structural and functional assessments using different methods. The historical basis for studying the anterior segment of the eye and AHO in glaucoma is discussed. Structural studies of AHO are reviewed and include traditional pathological approaches to modern tools such as multi-model two-photon microscopy and optical coherence tomography. Functional assessment focuses on visualizing AHO itself through a variety of non-real-time and real-time techniques such as aqueous angiography. Implications of distal outflow resistance and segmental AHO are discussed with an emphasis on melding bench-side research to viable clinical applications. Through the development of an improved structure: function relationship for AHO in the anterior segment of the normal and diseased eye, a better understanding of the eye with improved therapeutics may be developed
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