Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation

Eekelen, Leander van; Pinckaers, Hans; Brand, Michiel van den; Hebeda, Konnie M.; Litjens, Geert

doi:10.1016/j.pathol.2021.07.011

Cited by 18 publications

(24 citation statements)

References 36 publications

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“…By applying their model on 130 bone marrow histopathology samples, they found an inverse correlation between cellularity, myelopoiesis, megakaryocytes and age. 32 To this date, none of these published systems have been reported in validated clinical implementations, although they hint toward promising and evolving technology in the field.…”

Section: Ai/ml Approaches To Object Detection and Classification In B...mentioning

confidence: 99%

Applied machine learning in hematopathology

Dehkharghanian

Tizhoosh

et al. 2023

Int J Lab Hematology

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An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.

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Section: Ai/ml Approaches To Object Detection and Classification In B...mentioning

confidence: 99%

Applied machine learning in hematopathology

Dehkharghanian

Tizhoosh

et al. 2023

Int J Lab Hematology

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“…Here they classify lung cancer based on the patch-based classification and the slice-based classification. Van Eekelen L et al [5] here worked for the segmentation of the major cell and the tissue types in the bone marrow trephine biopsy images. Along with the input they have biopsy images for the different wide range of persons.…”

Section: Related Workmentioning

confidence: 99%

“…Convolutional layer output can be obtained by performing the below Eq. ( 5), (5) i represents the input image dimension i x i, f represents the filter size f, p represents the padding value, s represents stride value. After the convolutional layer, the output size is calculated to be 3x3, which is the result of the operations of padding, stride, and convolution.…”

Section: Proposed Model Architecture 1) Neural Networkmentioning

confidence: 99%

Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model

Abinaya¹,

Sivakumar²

2022

IJACSA

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Cancer is a major origin of mortality around the globe, responsible for roughly high morbidity and mortality in 2020, or almost one per six deaths. Cervical, lung, and breast are the most common types of cancers. Cervical is the fourth highest common in women worldwide. Cervical would then kill approximately 4,280 women. Infections that cause, such as human papillomavirus (HPV) and hepatitis, account for approximately 30% of cases in low-and lower-middle-income countries. Many cancers are curable if detected as early as possible. In this proposed work, developed the DA-Deep convnets model (Data augmentation with a deep, Convolutional Neural Network) for the detection of cervical cancer from biopsy images. Deep Convolutional Neural Network presents one of the most applied DL approaches in medical imaging. Today, enhancements in image analysis and processing, particularly medical imaging, have become a major factor in the improvement of various systems in areas such as medical prognosis, treatment, and diagnosis. Based on our proposed model we achieved 99.2% accuracy in detecting the input image has cancer or not.

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“…The % cellularity is calculated by dividing the total number of hits on the hematopoietic tissue by the sum of the counted hits on the fat and hematopoietic tissues, expressed as a percentage (25). The equivalent visual estimate of cellularity used in clinical routine assessment method is semi-quantitative (33) and, for highly trained individuals, it correlates with the point-counting method while being faster and simpler (20,25,26,34). However, it is still time-consuming, semi-quantitative, and may underestimate cellularity (13).…”

Section: Introductionmentioning

confidence: 99%

MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology

Naveiras

Burri

Royer-Chardon

et al. 2022

Preprint

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Bone marrow (BM) cellularity assessment is a crucial step in the evaluation of BM trephine biopsies for hematological and non-hematological disorders. Clinical assessment is based on a labor-intensive, semi-quantitative visual estimation of the hematopoietic and adipocytic components by hematopathologists, which does not provide quantitative information on other stromal compartments. In this study, we developed and validated MarrowQuant 2.0, an efficient user-friendly digital hematopathology workflow integrated within QuPath software which serves as BM quantifier for five mutually-exclusive compartments (bone, hematopoietic, adipocytic, interstitial/microvasculature areas, and “Other”) to derive cellularity of human BM trephine biopsies. Instance segmentation of individual adipocytes is realized via adaptation of the machine-learning-based algorithm StarDist. We calculated BM compartments and adipocyte size distributions of haematoxylin and eosin (H&E) images obtained from a total of 250 bone specimens, from control and acute myeloid leukemia or myelodysplastic patients at diagnosis or follow-up, then measured the agreement of cellularity estimates by MarrowQuant 2.0 against visual scores from four hematopathologists. The algorithm was capable of robust BM compartment segmentation with average mask accuracy of 86%, maximal for bone (99%), hematopoietic (92%) and adipocyte (98%) areas. MarrowQuant 2.0 cellularity score and hematopathologist estimations were highly correlated (R2 = 0.92–0.98, Intraclass-Correlation-Coefficient ICC = 0.98; inter-observer ICC 0.96). BM compartment segmentation quantitatively confirmed reciprocity of the hematopoietic and adipocytic compartments. MarrowQuant 2.0 performance was additionally tested for cellularity assessment of specimens prospectively collected from clinical routine diagnosis. After special consideration for the choice of the cellularity equation in specimens with expanded stroma, performance was similar in this setting (R2 = 0.86, n = 42). We thus conclude that MarrowQuant 2.0 can be applied in a clinical setting. We expect this workflow will contribute to improving the speed and ease of diagnosis in hematopathology and serve as a clinical research tool to explore novel biomarkers related to BM stromal components.

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Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation

Cited by 18 publications

References 36 publications

Applied machine learning in hematopathology

Applied machine learning in hematopathology

Diagnosis of Carcinoma from Histopathology Images using DA-Deep Convnets Model

MarrowQuant 2.0: a digital pathology workflow assisting bone marrow evaluation in clinical and experimental hematology

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