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Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep
Laura M G van Huizen1, Max Blokker1, Yael Rip1
1LaserLab Amsterdam, Department of Physics, Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
Insights
Label-free microscopy combined with deep learning accurately identifies and quantifies leukocytes in bronchoalveolar lavage fluid (BALF). This advanced technique offers faster diagnosis for interstitial lung diseases (ILDs), reducing costs and workload.
Area of Science:
- Medical imaging
- Computational pathology
- Immunology
Background:
- Interstitial lung diseases (ILDs) diagnosis relies on analyzing bronchoalveolar lavage fluid (BALF) and biopsies.
- Standard leukocyte differentiation in BALF is labor-intensive and time-consuming.
- Third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy show promise for leukocyte identification in blood.
Purpose of the Study:
- Extend leukocyte differentiation to BALF samples using THG/MPEF microscopy.
- Develop a deep learning algorithm for automated leukocyte identification and quantification in BALF.
Main Methods:
- Isolated leukocytes from blood and BALF samples.
- Imaged cells using label-free THG/MPEF microscopy.
- Trained a deep learning model on 2D images to estimate leukocyte ratios.
Main Results:
- Identified distinct leukocyte populations in BALF using label-free microscopy.
- Deep learning network achieved >90% accuracy in estimating leukocyte percentages on BALF samples.
- Demonstrated distinctive cytological characteristics for neutrophils, eosinophils, lymphocytes, and macrophages.
Conclusions:
- Label-free THG/MPEF microscopy and deep learning enable rapid leukocyte differentiation and quantification.
- This approach can accelerate ILD diagnosis, decrease costs, and reduce inter-observer variability.
- Automated analysis of BALF offers significant potential for clinical diagnostics.
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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