Dataset Information

Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (CLN3 disease).

ABSTRACT: Quantifying lymphocyte vacuolization in peripheral blood smears (PBSs) serves as a measure for disease severity in CLN3 disease-a lysosomal storage disorder of childhood-onset. However, thus far quantification methods are based on labor-intensive manual assessment of PBSs. As machine learning techniques like convolutional neural networks (CNNs) have been deployed quite successfully in detecting pathological features in PBSs, we explored whether these techniques could be utilized to automate quantification of lymphocyte vacuolization. Here, we present and validate a deep learning pipeline that automates quantification of lymphocyte vacuolization. By using two CNNs in succession, trained for cytoplasm-segmentation and vacuolization-detection, respectively, we obtained an excellent correlation with manual quantification of lymphocyte vacuolization (r = 0.98, n = 40). These results show that CNNs can be utilized to automate the otherwise cumbersome task of manually quantifying lymphocyte vacuolization, thereby aiding prompt clinical decisions in relation to CLN3 disease, and potentially beyond.

SUBMITTER: Nonkes LJP

PROVIDER: S-EPMC7932860 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (CLN3 disease).

Nonkes Lourens J P LJP Kuper Willemijn F E WFE Berrens-Hogenbirk Karin K Musson Ruben E A REA van Hasselt Peter M PM Huisman Albert A

JIMD reports 20210125 1

Quantifying lymphocyte vacuolization in peripheral blood smears (PBSs) serves as a measure for disease severity in CLN3 disease-a lysosomal storage disorder of childhood-onset. However, thus far quantification methods are based on labor-intensive manual assessment of PBSs. As machine learning techniques like convolutional neural networks (CNNs) have been deployed quite successfully in detecting pathological features in PBSs, we explored whether these techniques could be utilized to automate quan ...[more]

PMID: 33728252

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Project description:BackgroundThe beating heart is the generator of blood flow through the cardiovascular system. Within the heart's own chambers, normal complex blood flow patterns can be disturbed by diseases. Methods for the quantification of intra-cardiac blood flow, with its 4D (3D+time) nature, are lacking. We sought to develop and validate a novel semi-automatic analysis approach that integrates flow and morphological data.MethodIn six healthy subjects and three patients with dilated cardiomyopathy, three-directional, three-dimensional cine phase-contrast cardiovascular magnetic resonance (CMR) velocity data and balanced steady-state free-precession long- and short-axis images were acquired. The LV endocardium was segmented from the short-axis images at the times of isovolumetric contraction (IVC) and isovolumetric relaxation (IVR). At the time of IVC, pathlines were emitted from the IVC LV blood volume and traced forwards and backwards in time until IVR, thus including the entire cardiac cycle. The IVR volume was used to determine if and where the pathlines left the LV. This information was used to automatically separate the pathlines into four different components of flow: Direct Flow, Retained Inflow, Delayed Ejection Flow and Residual Volume. Blood volumes were calculated for every component by multiplying the number of pathlines with the blood volume represented by each pathline. The accuracy and inter- and intra-observer reproducibility of the approach were evaluated by analyzing volumes of LV inflow and outflow, the four flow components, and the end-diastolic volume.ResultsThe volume and distribution of the LV flow components were determined in all subjects. The calculated LV outflow volumes [ml] (67 +/- 13) appeared to fall in between those obtained by through-plane phase-contrast CMR (77 +/- 16) and Doppler ultrasound (58 +/- 10), respectively. Calculated volumes of LV inflow (68 +/- 11) and outflow (67 +/- 13) were well matched (NS). Low inter- and intra-observer variability for the assessment of the volumes of the flow components was obtained.ConclusionsThis semi-automatic analysis approach for the quantification of 4D blood flow resulted in accurate LV inflow and outflow volumes and a high reproducibility for the assessment of LV flow components.

Project description:BackgroundMalaria parasitemia is commonly used as a measurement of the amount of parasites in the patient's blood and a crucial indicator for the degree of infection. Manual evaluation of Giemsa-stained thin blood smears under the microscope is onerous, time consuming and subject to human error. Although automatic assessments can overcome some of these problems the available methods are currently limited by their inability to evaluate cases that deviate from a chosen "standard" model.ResultsIn this study reliable parasitemia counts were achieved even for sub-standard smear and image quality. The outcome was assessed through comparisons with manual evaluations of more than 200 sample smears and related to the complexity of cell overlaps. On average an estimation error of less than 1% with respect to the average of manually obtained parasitemia counts was achieved. In particular the results from the proposed approach are generally within one standard deviation of the counts provided by a comparison group of malariologists yielding a correlation of 0.97. Variations occur mainly for blurred out-of-focus imagery exhibiting larger degrees of cell overlaps in clusters of erythrocytes. The assessment was also carried out in terms of precision and recall and combined in the F-measure providing results generally in the range of 92% to 97% for a variety of smears. In this context the observed trade-off relation between precision and recall guaranteed stable results. Finally, relating the F-measure with the degree of cell overlaps, showed that up to 50% total cell overlap can be tolerated if the smear image is well-focused and the smear itself adequately stained.ConclusionThe automatic analysis has proven to be comparable with manual evaluations in terms of accuracy. Moreover, the test results have shown that the proposed comparison-based approach, by exploiting the interrelation between different images and color channels, has successfully overcome most of the inherent limitations possibly occurring during the sample preparation and image acquisition phase. Eventually, this can be seen as an opportunity for developing low-cost solutions for mass screening.

Dataset Information

Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (CLN3 disease).

Publications

Automatic quantification of lymphocyte vacuolization in peripheral blood smears of patients with Batten's disease (CLN3 disease).

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