Segmentation of nucleus and cytoplasm of a single cell in three-dimensional tomogram using optical coherence tomography

Skin cancer margin detection using nanosensitive optical coherence tomography and a comparative study with confocal microscopy

Excision biopsy and histology represent the gold standard for morphological investigation of the skin, in particular for cancer diagnostics. Nevertheless, a biopsy may alter the original morphology, usually requires several weeks for results, is non-repeatable on the same site and always requires an iatrogenic trauma. Hence, diagnosis and clinical management of diseases may be substantially improved by new non-invasive imaging techniques. Optical Coherence Tomography (OCT) is a non-invasive depth-resolved optical imaging modality based on low coherence interferometry that enables high-resolution, cross-sectional imaging in biological tissues and it can be used to obtain both structural and functional information. Beyond the resolution limit, it is not possible to detect structural and functional information using conventional OCT. In this paper, we present a recently developed technique, nanosensitive OCT (nsOCT), improved using broadband supercontinuum laser, and demonstrate nanoscale sensitivity to structural changes within ex vivo human skin tissue. The extended spectral bandwidth permitted access to a wider distribution of spatial frequencies and improved the dynamic range of the nsOCT. Firstly, we demonstrate numerical and experimental detection of a few nanometers structural difference using the nsOCT method from single B-scan images of phantoms with sub-micron periodic structures, acting like Bragg gratings, along the depth. Secondly, our study shows that nsOCT can distinguish nanoscale structural changes at the skin cancer margin from the healthy region in en face images at clinically relevant depths. Finally, we compare the nsOCT en face image with a high-resolution confocal microscopy image to confirm the structural differences between the healthy and lesional/cancerous regions, allowing the detection of the skin cancer margin.

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.

Three-dimensional nucleus-to-cytoplasm ratios provide better discrimination of normal and lung adenocarcinoma cells than in two dimensions

We acquired multiphoton images of normal and lung adenocarcinoma cell lines in three dimensions. Image stacks of the cells were then processed to obtain nucleus-to-cytoplasm (N/C) ratios in two and three dimensions. While N/C ratios in three dimensions can be unambiguously determined from the volumetric ratios of the nucleus and cytoplasm, two-dimensional (2-D) N/C can vary depending on the axial plane selected for N/C ratio determination. We determined 2-D N/C ratios from three criteria: (1) axial position at which the nuclear area is the largest; (2) the largest 2-D N/C ratio value; and (3) axial position at the midpoint of nuclear axial position. We found that different definitions of 2-D N/C ratio will significantly affect its value. Furthermore, in general, larger variance was found in 2-D rather than three-dimensional (3-D) N/C ratios. Lack of ambiguity in definition and reduced variance suggest that 3-D N/C ratio is a better parameter for characterizing tumor cells in the clinical setting.

3D cell electrorotation and imaging for measuring multiple cellular biophysical properties

3D rotation is one of many fundamental manipulations to cells and imperative in a wide range of applications in single cell analysis involving biology, chemistry, physics and medicine. In this article, we report a dielectrophoresis-based, on-chip manipulation method that can load and rotate a single cell for 3D cell imaging and multiple biophysical property measurements. To achieve this, we trapped a single cell in constriction and subsequently released it to a rotation chamber formed by four sidewall electrodes and one transparent bottom electrode. In the rotation chamber, rotating electric fields were generated by applying appropriate AC signals to the electrodes for driving the single cell to rotate in 3D under control. The rotation spectrum for in-plane rotation was used to extract the cellular dielectric properties based on a spherical single-shell model, and the stacked images of out-of-plane cell rotation were used to reconstruct the 3D cell morphology to determine its geometric parameters. We have tested the capabilities of our method by rotating four representative mammalian cells including HeLa, C3H10, B lymphocyte, and HepaRG. Using our device, we quantified the area-specific membrane capacitance and cytoplasm conductivity for the four cells, and revealed the subtle difference of geometric parameters (i.e., surface area, volume, and roughness) by 3D cell imaging of cancer cells and normal leukocytes. Combining microfluidics, dielectrophoresis, and microscopic imaging techniques, our electrorotation-on-chip (EOC) technique is a versatile method for manipulating single cells under investigation and measuring their multiple biophysical properties.