A robust cell counting approach based on a normalized 2D cross-correlation scheme for in-line holographic images

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning

We present sensing time-lapse morphogenesis of living bone cells under micro-fluidic shear stress (FSS) by digital holographic (DH) microscopy. To remove the effect of aberrations on quantitative measurements, we propose a numerical and automatic method to compensate for aberrations based on a convolutional neural network (CNN). For the first time, the aberration compensation issue is considered as a regression task where optimal coefficients for constructing the phase aberration map act as responses corresponding to the input aberrated phase image. We adopted tens of thousands of living cells' phase images reconstructed from digital holograms for training the CNN. The experiments demonstrate that, based on the trained network, phase aberrations can be totally removed in real-time without any hypothesis of object and aberration phase, knowledge of the setup's physical parameters, and the operation of selecting background regions; hence, the morphogenesis of the bone cells under FSS is accurately detected and quantitatively analyzed. The results show that the proposed method could provide a highly efficient and versatile way to investigate the effects of micro-FSS on living biological cells in microfluidic lab-on-chip platforms thanks to the combination of phase-contrast label-free microcopy with artificial intelligence.

Human-level blood cell counting on lens-free shadow images exploiting deep neural networks

In-line holographic microscopes paved the way for realizing portable cell counting systems using deep neural networks.

Improving reliability of live/dead cell counting through automated image mosaicing

Cell counting is one of the basic needs of most biological experiments. Numerous methods and systems have been studied to improve the reliability of counting. However, at present, manual cell counting performed with a hemocytometer still represents the gold standard, despite several problems limiting reproducibility and repeatability of the counts and, at the end, jeopardizing their reliability in general. We present our own approach based on image processing techniques to improve counting reliability. It works in two stages: first building a high-resolution image of the hemocytometer's grid, then counting the live and dead cells by tagging the image with flags of different colours. In particular, we introduce GridMos (http://sourceforge.net/p/gridmos), a fully-automated mosaicing method to obtain a mosaic representing the whole hemocytometer's grid. In addition to offering more significant statistics, the mosaic "freezes" the culture status, thus permitting analysis by more than one operator. Finally, the mosaic achieved can thus be tagged by using an image editor, thus markedly improving counting reliability. The experiments performed confirm the improvements brought about by the proposed counting approach in terms of both reproducibility and repeatability, also suggesting the use of a mosaic of an entire hemocytometer's grid, then labelled trough an image editor, as the best likely candidate for the new gold standard method in cell counting.

Optimization of a cell counting algorithm for mobile point-of-care testing platforms

In a point-of-care (POC) setting, it is critically important to reliably count the number of specific cells in a blood sample. Software-based cell counting, which is far faster than manual counting, while much cheaper than hardware-based counting, has emerged as an attractive solution potentially applicable to mobile POC testing. However, the existing software-based algorithm based on the normalized cross-correlation (NCC) method is too time- and, thus, energy-consuming to be deployed for battery-powered mobile POC testing platforms. In this paper, we identify inefficiencies in the NCC-based algorithm and propose two synergistic optimization techniques that can considerably reduce the runtime and, thus, energy consumption of the original algorithm with negligible impact on counting accuracy. We demonstrate that an Android™ smart phone running the optimized algorithm consumes 11.5× less runtime than the original algorithm.

Imaging adherent cells in the microfluidic channel hidden by flowing RBCs as occluding objects by a holographic method

Imaging through turbid media is a challenging topic. A liquid is considered turbid when dispersed particles provoke strong light scattering, thus destroying the image formation by any standard optical system. Generally, colloidal solutions belong to the class of turbid media since dispersed particles have dimensions ranging between 0.2 μm and 2 μm. However, in microfluidics, another relevant issue has to be considered in the case of flowing liquid made of a multitude of occluding objects, e.g. red blood cells (RBCs) flowing in veins. In such a case instead of severe scattering processes unpredictable phase delays occur resulting in a wavefront distortion, thus disturbing or even hindering the image formation of objects behind such obstructing layer. In fact RBCs can be considered to be thin transparent phase objects. Here we show that sharp amplitude imaging and phase-contrast mapping of cells hidden behind biological occluding objects, namely RBCs, is possible in harsh noise conditions and with a large field-of view by Multi-Look Digital Holography microscopy (ML-DH). Noteworthy, we demonstrate that ML-DH benefits from the presence of the RBCs, providing enhancement in terms of numerical resolution and noise suppression thus obtaining images whose quality is higher than the quality achievable in the case of a liquid without occlusive objects.