Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue

Shi, Jindou; Tu, Haohua; Park, Jaena; Marjanović, Marina; Higham, Anna; Luckey, Natasha N.; Cradock, Kimberly A.; Liu, Z. George; Boppart, Stephen A.

doi:10.1101/2022.05.24.493356

Cited by 2 publications

(2 citation statements)

References 63 publications

(113 reference statements)

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“…Deep learning allows development of cell counters based on the image data. Studies have shown that the label-free images are suitable for automatic image analysis using deep learning networks [15][16][17]. These studies have used deep learning networks to classify malignant tissue from healthy tissue and to classify activated lymphocytes, but not for cell…”

Section: Introductionmentioning

confidence: 99%

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

et al. 2023

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Background In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Objective To extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification. Methods Leukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference. Results Different leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set. Conclusions Label-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations.

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Section: Introductionmentioning

confidence: 99%

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

et al. 2023

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“…Deep learning allows development of cell counters based on the image data. Studies have shown that the label-free images are suitable for automatic image analysis using deep learning networks [14][15][16]. These studies have used deep learning networks to classify malignant tissue from healthy tissue and to classify activated lymphocytes, but not for cell counting.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of leukocytes in bronchoalveolar lavage fluid samples using higher harmonic generation microscopy and deep learning

Huizen

Blokker

Rip

et al. 2022

Preprint

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In many diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. In BALF the differentiation of neutrophils, eosinophils, lymphocytes, and macrophages can contribute to diagnose the underlying ILD entity. To analyze the BALF standard cytological techniques are labor-intensive and time-consuming. Studies have shown promising cell identification performance on blood fractions analyzed by third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy. Here, we extend this to BALF samples, and we trained a deep learning algorithm for automated analysis on the image level against reference cytology. We imaged blood fractions from three healthy individuals and one asthma patients, and six BALFs from ILD patients. We determined the leukocyte characteristics in terms of cellular and nuclear morphology, and THG and MPEF signal intensity. A deep learning model was trained on both blood fractions and BALF 2D images was used to estimate the leukocyte ratios by using only the standard cytology differential cell ratios at the image-level as reference. The deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, coming within a 2 to 10% margin in BALF samples in the hold-out testing set. We suggest that the performance of the combined label free imaging and AI analysis can be improved further by collecting 3D data and data of additional fluid samples of various ILD diseases and healthy samples, THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios will not only speed-up the diagnostic process but can also reduce costs, the workload and reduce inter-observer variations.

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Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue

Cited by 2 publications

References 63 publications

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning

Differentiation of leukocytes in bronchoalveolar lavage fluid samples using higher harmonic generation microscopy and deep learning

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