Dataset Information

Fully automated stroke tissue estimation using random forest classifiers (FASTER).

ABSTRACT: Several clinical trials have recently proven the efficacy of mechanical thrombectomy for treating ischemic stroke, within a six-hour window for therapy. To move beyond treatment windows and toward personalized risk assessment, it is essential to accurately identify the extent of tissue-at-risk ("penumbra"). We introduce a fully automated method to estimate the penumbra volume using multimodal MRI (diffusion-weighted imaging, a T2w- and T1w contrast-enhanced sequence, and dynamic susceptibility contrast perfusion MRI). The method estimates tissue-at-risk by predicting tissue damage in the case of both persistent occlusion and of complete recanalization. When applied to 19 test cases with a thrombolysis in cerebral infarction grading of 1-2a, mean overestimation of final lesion volume was 30?ml, compared with 121?ml for manually corrected thresholding. Predicted tissue-at-risk volume was positively correlated with final lesion volume ( p?

SUBMITTER: McKinley R

PROVIDER: S-EPMC5536784 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Fully automated stroke tissue estimation using random forest classifiers (FASTER).

McKinley Richard R Häni Levin L Gralla Jan J El-Koussy M M Bauer S S Arnold M M Fischer U U Jung S S Mattmann Kaspar K Reyes Mauricio M Wiest Roland R

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20160101 8

Several clinical trials have recently proven the efficacy of mechanical thrombectomy for treating ischemic stroke, within a six-hour window for therapy. To move beyond treatment windows and toward personalized risk assessment, it is essential to accurately identify the extent of tissue-at-risk ("penumbra"). We introduce a fully automated method to estimate the penumbra volume using multimodal MRI (diffusion-weighted imaging, a T2w- and T1w contrast-enhanced sequence, and dynamic susceptibility c ...[more]

PMID: 27798267

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Project description:BackgroundThe conventional method for the diagnosis of malaria parasites is the microscopic examination of stained blood films, which is time consuming and requires expertise. We introduce computer-based image segmentation and life stage classification with a random forest classifier. Segmentation and stage classification are performed on a large dataset of malaria parasites with ground truth labels provided by experts.MethodsWe made use of Giemsa stained images obtained from the blood of 16 patients infected with Plasmodium falciparum. Experts labeled the parasite types from each of the images. We applied a two-step approach: image segmentation followed by life stage classification. In segmentation, we classified each pixel as a parasite or non-parasite pixel using a random forest classifier. Performance was evaluated with classification accuracy, Dice coefficient and free-response receiver operating characteristic (FROC) analysis. In life stage classification, we classified each of the segmented objects into one of 8 classes: 6 parasite life stages, early ring, late ring or early trophozoite, mid trophozoite, early schizont, late schizont or segmented, and two other classes, white blood cell or debris.ResultsOur segmentation method gives an average cross-validated Dice coefficient of 0.82 which is a 13% improvement compared to the Otsu method. The Otsu method achieved a True Positive Fraction (TPF) of 0.925 at the expense of a False Positive Rate (FPR) of 2.45. At the same TPF of 0.925, our method achieved an FPR of 0.92, an improvement by more than a factor two. We find that inclusion of average intensity of the whole image as feature for the random forest considerably improves segmentation performance. We obtain an overall accuracy of 58.8% when classifying all life stages. Stages are mostly confused with their neighboring stages. When we reduce the life stages to ring, trophozoite and schizont only, we obtain an accuracy of 82.7%.ConclusionPixel classification gives better segmentation performance than the conventional Otsu method. Effects of staining and background variations can be reduced with the inclusion of average intensity features. The proposed method and data set can be used in the development of automatic tools for the detection and stage classification of malaria parasites. The data set is publicly available as a benchmark for future studies.

Project description:BackgroundComposition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists.ObjectiveThis study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks.MethodsA data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical's Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices.ResultsA poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality.ConclusionsThe interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.

Dataset Information

Fully automated stroke tissue estimation using random forest classifiers (FASTER).

Publications

Fully automated stroke tissue estimation using random forest classifiers (FASTER).

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