Research in morphology and flow cytometry is at the heart of hematology

Béné, Marie C.; Zini, Gina

doi:10.3324/haematol.2016.163147

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…Microscopic analysis of blood films is fundamental to many areas of haematology from research to clinical diagnosis [1]. Automated assessment of digitized blood films [2,3] has potential to transform overstretched clinical services that require prompt and accurate assessment of large numbers of specimens.…”

Section: Introductionmentioning

confidence: 99%

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Shaw

Claveau

Manescu

et al. 2021

The Journal of Pathology

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Automated image‐based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade‐off between magnification and spatial resolution. To address this, we investigated three state‐of‐the‐art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high‐resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high‐resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa‐stained peripheral blood smears. Using computational image analysis and shape‐based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood‐borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

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Section: Introductionmentioning

confidence: 99%

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Shaw

Claveau

Manescu

et al. 2021

The Journal of Pathology

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“…Analysis of peripheral blood film (PBF) specimens under an optical microscope is essential to haematological pathways both in low-and high-income settings. 6 Contrary to methods such as flow cytometry, Coulter counters or antibody-based rapid diagnosis tests, optical microscopy allows visualization and analysis of cell morphology, which is crucial in assessing both communicable and non-communicable diseases, a fact that is especially important in resource constrained settings. 7 Recent advances in computational pathology have the potential to scale up Artificial Intelligence assisted examination of PBF for diagnostic decision support.…”

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confidence: 99%

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia

Moysis,

Brown,

Shokunbi

et al. 2024

Br J Haematol

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SummaryIn sub‐Saharan Africa, acute‐onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the spleen, leading to the presence of distinct red blood cells (RBCs) with altered morphological characteristics. We hypothesized that these RBCs could be detected systematically and at scale in peripheral blood films (PBFs) by harnessing the capabilities of deep learning models. Assessment of PBFs by a microscopist does not scale for this task and is subject to variability. Here we introduce a deep learning model, leveraging a weakly supervised Multiple Instance Learning framework, to Identify SMA (MILISMA) through the presence of morphologically changed RBCs. MILISMA achieved a classification accuracy of 83% (receiver operating characteristic area under the curve [AUC] of 87%; precision‐recall AUC of 76%). More importantly, MILISMA's capabilities extend to identifying statistically significant morphological distinctions (p < 0.01) in RBCs descriptors. Our findings are enriched by visual analyses, which underscore the unique morphological features of SMA‐affected RBCs when compared to non‐SMA cells. This model aided detection and characterization of RBC alterations could enhance the understanding of SMA's pathology and refine SMA diagnostic and prognostic evaluation processes at scale.

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Content aware multi-focus image fusion for high-magnification blood film microscopy

Manescu

Shaw

Zajiczek

et al. 2022

Biomed. Opt. Express

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Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required.

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Research in morphology and flow cytometry is at the heart of hematology

Abstract: nous leukemia in mice by the P210bcr/abl gene of the Philadelphia chromosome. Science. 1990;247(4944)

Cited by 3 publications

References 16 publications

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia

Content aware multi-focus image fusion for high-magnification blood film microscopy

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