31 research outputs found
CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patient With Suspected Ischemic Stroke
Precise and fast prediction methods for ischemic areas comprised of dead
tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS)
patients are of significant clinical interest. They play an essential role in
improving diagnosis and treatment planning. Computed Tomography (CT) scan is
one of the primary modalities for early assessment in patients with suspected
AIS. CT Perfusion (CTP) is often used as a primary assessment to determine
stroke location, severity, and volume of ischemic lesions. Current automatic
segmentation methods for CTP mostly use already processed 3D parametric maps
conventionally used for clinical interpretation by radiologists as input.
Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time
input, where the spatial information over the volume is ignored. In addition,
these methods are only interested in segmenting core regions, while predicting
penumbra can be essential for treatment planning. This paper investigates
different methods to utilize the entire 4D CTP as input to fully exploit the
spatio-temporal information, leading us to propose a novel 4D convolution
layer. Our comprehensive experiments on a local dataset of 152 patients divided
into three groups show that our proposed models generate more precise results
than other methods explored. Adopting the proposed 4D mJ-Net, a Dice
Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core
areas, respectively. The code is available on
https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git
AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning
Perfusion imaging is crucial in acute ischemic stroke for quantifying the
salvageable penumbra and irreversibly damaged core lesions. As such, it helps
clinicians to decide on the optimal reperfusion treatment. In perfusion CT
imaging, deconvolution methods are used to obtain clinically interpretable
perfusion parameters that allow identifying brain tissue abnormalities.
Deconvolution methods require the selection of two reference vascular functions
as inputs to the model: the arterial input function (AIF) and the venous output
function, with the AIF as the most critical model input. When manually
performed, the vascular function selection is time demanding, suffers from poor
reproducibility and is subject to the professionals' experience. This leads to
potentially unreliable quantification of the penumbra and core lesions and,
hence, might harm the treatment decision process. In this work we automatize
the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable
deep learning approach for estimating the vascular functions. Unlike previous
methods using clustering or segmentation techniques to select vascular voxels,
AIFNet is directly optimized at the vascular function estimation, which allows
to better recognise the time-curve profiles. Validation on the public ISLES18
stroke database shows that AIFNet reaches inter-rater performance for the
vascular function estimation and, subsequently, for the parameter maps and core
lesion quantification obtained through deconvolution. We conclude that AIFNet
has potential for clinical transfer and could be incorporated in perfusion
deconvolution software.Comment: Preprint submitted to Elsevie
A Review on Computer Aided Diagnosis of Acute Brain Stroke.
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas
Final infarct prediction in acute ischemic stroke
This article focuses on the control center of each human body: the brain. We
will point out the pivotal role of the cerebral vasculature and how its complex
mechanisms may vary between subjects. We then emphasize a specific acute
pathological state, i.e., acute ischemic stroke, and show how medical imaging
and its analysis can be used to define the treatment. We show how the
core-penumbra concept is used in practice using mismatch criteria and how
machine learning can be used to make predictions of the final infarct, either
via deconvolution or convolutional neural networks.Comment: 17 pages, 5 figures, part of PhD thesis KU Leuven 2022 "Understanding
Final Infarct Prediction in Acute Ischemic Stroke Using Convolutional Neural
Networks
Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge.
BACKGROUND AND PURPOSE
The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on computed tomography perfusion (CTP) is often necessary to determine eligibility for late-time-window thrombectomy. Therefore, the aim of ISLES-2018 was to segment infarcted tissue on CTP based on diffusion-weighted imaging as a reference standard.
METHODS
The data, from 4 centers, consisted of 103 cases of acute anterior circulation large artery occlusion stroke who underwent diffusion-weighted imaging rapidly after CTP. Diffusion-weighted imaging lesion segmentation was performed manually and acted as a reference standard. The data were separated into 63 cases for training and 40 for testing, upon which quality metrics (dice score coefficient, Hausdorff distance, absolute lesion volume difference, etc) were computed to rank methods based on their overall performance.
RESULTS
Twenty-four different teams participated in the challenge. Median time to CTP was 185 minutes (interquartile range, 180-238), the time between CTP and magnetic resonance imaging was 36 minutes (interquartile range, 25-79), and the median infarct lesion size was 15.2 mL (interquartile range, 5.7-45). The best performance for Dice score coefficient and absolute volume difference were 0.51 and 10.1 mL, respectively, from different teams. Based on the ranking criteria, the top team's algorithm demonstrated for average Dice score coefficient and average absolute volume difference 0.51 and 10.2 mL, respectively, outperforming the conventional threshold-based method (dice score coefficient, 0.3; volume difference, 15.3). Diverse algorithms were used, almost all based on deep learning, with top-ranked approaches making use of the raw perfusion data as well as methods to synthetically generate complementary information to boost prediction performance.
CONCLUSIONS
Machine learning methods may predict infarcted tissue from CTP with improved accuracy compared with threshold-based methods used in clinical routine. This dataset will remain public and can be used to test improvement in algorithms over time
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
The clinical interest is often to measure the volume of a structure, which is
typically derived from a segmentation. In order to evaluate and compare
segmentation methods, the similarity between a segmentation and a predefined
ground truth is measured using popular discrete metrics, such as the Dice
score. Recent segmentation methods use a differentiable surrogate metric, such
as soft Dice, as part of the loss function during the learning phase. In this
work, we first briefly describe how to derive volume estimates from a
segmentation that is, potentially, inherently uncertain or ambiguous. This is
followed by a theoretical analysis and an experimental validation linking the
inherent uncertainty to common loss functions for training CNNs, namely
cross-entropy and soft Dice. We find that, even though soft Dice optimization
leads to an improved performance with respect to the Dice score and other
measures, it may introduce a volume bias for tasks with high inherent
uncertainty. These findings indicate some of the method's clinical limitations
and suggest doing a closer ad-hoc volume analysis with an optional
re-calibration step.Comment: 18 pages, 7 figures, 3 tables, published in Elsevier Medical Image
Analysis (2021
Theoretical analysis and experimental validation of volume bias of soft Dice optimized segmentation maps in the context of inherent uncertainty
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using popular discrete metrics, such as the Dice score. Recent segmentation methods use a differentiable surrogate metric, such as soft Dice, as part of the loss function during the learning phase. In this work, we first briefly describe how to derive volume estimates from a segmentation that is, potentially, inherently uncertain or ambiguous. This is followed by a theoretical analysis and an experimental validation linking the inherent uncertainty to common loss functions for training CNNs, namely cross-entropy and soft Dice. We find that, even though soft Dice optimization leads to an improved performance with respect to the Dice score and other measures, it may introduce a volume bias for tasks with high inherent uncertainty. These findings indicate some of the method’s clinical limitations and suggest doing a closer ad-hoc volume analysis with an optional re-calibration step.NEXIS (www.nexis-project.eu), a project that has received funding from the European Union’s Horizon 2020 Research and Innovations Programme and an innovation mandate of Flanders Innovation and Entrepreneurship (VLAIO).http://www.elsevier.com/locate/mediahj2022Anatom