7,402 research outputs found
autoTICI: Automatic Brain Tissue Reperfusion Scoring on 2D DSA Images of Acute Ischemic Stroke Patients
The Thrombolysis in Cerebral Infarction (TICI) score is an important metric
for reperfusion therapy assessment in acute ischemic stroke. It is commonly
used as a technical outcome measure after endovascular treatment (EVT).
Existing TICI scores are defined in coarse ordinal grades based on visual
inspection, leading to inter- and intra-observer variation. In this work, we
present autoTICI, an automatic and quantitative TICI scoring method. First,
each digital subtraction angiography (DSA) sequence is separated into four
phases (non-contrast, arterial, parenchymal and venous phase) using a
multi-path convolutional neural network (CNN), which exploits spatio-temporal
features. The network also incorporates sequence level label dependencies in
the form of a state-transition matrix. Next, a minimum intensity map (MINIP) is
computed using the motion corrected arterial and parenchymal frames. On the
MINIP image, vessel, perfusion and background pixels are segmented. Finally, we
quantify the autoTICI score as the ratio of reperfused pixels after EVT. On a
routinely acquired multi-center dataset, the proposed autoTICI shows good
correlation with the extended TICI (eTICI) reference with an average area under
the curve (AUC) score of 0.81. The AUC score is 0.90 with respect to the
dichotomized eTICI. In terms of clinical outcome prediction, we demonstrate
that autoTICI is overall comparable to eTICI.Comment: 10 pages; submitted to IEEE TM
3D Rigid Registration of Intraoperative Ultrasound and Preoperative MR Brain Images Based on Hyperechogenic Structures
The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of
information contained in each image modality. To overcome this difficulty, we
introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use
In vivo measurement of human brain elasticity using a light aspiration device
The brain deformation that occurs during neurosurgery is a serious issue
impacting the patient "safety" as well as the invasiveness of the brain
surgery. Model-driven compensation is a realistic and efficient solution to
solve this problem. However, a vital issue is the lack of reliable and easily
obtainable patient-specific mechanical characteristics of the brain which,
according to clinicians' experience, can vary considerably. We designed an
aspiration device that is able to meet the very rigorous sterilization and
handling process imposed during surgery, and especially neurosurgery. The
device, which has no electronic component, is simple, light and can be
considered as an ancillary instrument. The deformation of the aspirated tissue
is imaged via a mirror using an external camera. This paper describes the
experimental setup as well as its use during a specific neurosurgery. The
experimental data was used to calibrate a continuous model. We show that we
were able to extract an in vivo constitutive law of the brain elasticity: thus
for the first time, measurements are carried out per-operatively on the
patient, just before the resection of the brain parenchyma. This paper
discloses the results of a difficult experiment and provide for the first time
in-vivo data on human brain elasticity. The results point out the softness as
well as the highly non-linear behavior of the brain tissue.Comment: Medical Image Analysis (2009) accept\'
Grid simulation services for the medical community
The first part of this paper presents a selection of medical simulation applications, including image reconstruction, near real-time registration for neuro-surgery, enhanced dose distribution calculation for radio-therapy, inhaled drug delivery prediction, plastic surgery planning and cardio-vascular system simulation. The latter two topics are discussed in some detail. In the second part, we show how such services can be made available to the clinical practitioner using Grid technology. We discuss the developments and experience made during the EU project GEMSS, which provides reliable, efficient, secure and lawful medical Grid services
Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models
The health and function of tissue rely on its vasculature network to provide
reliable blood perfusion. Volumetric imaging approaches, such as multiphoton
microscopy, are able to generate detailed 3D images of blood vessels that could
contribute to our understanding of the role of vascular structure in normal
physiology and in disease mechanisms. The segmentation of vessels, a core image
analysis problem, is a bottleneck that has prevented the systematic comparison
of 3D vascular architecture across experimental populations. We explored the
use of convolutional neural networks to segment 3D vessels within volumetric in
vivo images acquired by multiphoton microscopy. We evaluated different network
architectures and machine learning techniques in the context of this
segmentation problem. We show that our optimized convolutional neural network
architecture, which we call DeepVess, yielded a segmentation accuracy that was
better than both the current state-of-the-art and a trained human annotator,
while also being orders of magnitude faster. To explore the effects of aging
and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of
cortical blood vessels in young and old mouse models of Alzheimer's disease and
wild type littermates. We found little difference in the distribution of
capillary diameter or tortuosity between these groups, but did note a decrease
in the number of longer capillary segments () in aged animals as
compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
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