Label-free cell classification in holographic flow cytometry through an unbiased learning strategy.

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

StaSiS-Net: A stacked and siamese disparity estimation network for depth reconstruction in modern 3D laparoscopy.

Developing accurate and real-time algorithms for a non-invasive three-dimensional representation and reconstruction of internal patient structures is one of the main research fields in computer-assisted surgery and endoscopy. Mono and stereo endoscopic images of soft tissues are converted into a three-dimensional representation by the estimation of depth maps. However, automatic, detailed, accurate and robust depth map estimation is a challenging problem that, in the stereo setting, is strictly dependent on a robust estimate of the disparity map. Many traditional algorithms are often inefficient or not accurate. In this work, novel self-supervised stacked and Siamese encoder/decoder neural networks are proposed to compute accurate disparity maps for 3D laparoscopy depth estimation. These networks run in real-time on standard GPU-equipped desktop computers and the outputs may be used for depth map estimation using the a known camera calibration. We compare performance on three different public datasets and on a new challenging simulated dataset and our solutions outperform state-of-the-art mono and stereo depth estimation methods. Extensive robustness and sensitivity analyses on more than 30000 frames has been performed. This work leads to important improvements in mono and stereo real-time depth map estimation of soft tissues and organs with a very low average mean absolute disparity reconstruction error with respect to ground truth.

Blind microscopy image denoising with a deep residual and multiscale encoder/decoder network.

In computer-aided diagnosis (CAD) focused on microscopy, denoising improves the quality of image analysis. In general, the accuracy of this process may depend both on the experience of the microscopist and on the equipment sensitivity and specificity. A medical image could be corrupted by several perturbations during image acquisition. Nowadays, CAD deep learning applications pre-process images with image denoising models to reinforce learning and prediction. In this work, an innovative and lightweight deep multiscale convolutional encoder-decoder neural network is proposed. Specifically, the encoder uses deterministic mapping to map features into a hidden representation. Then, the latent representation is rebuilt to generate the reconstructed denoised image. Residual learning strategies are used to improve and accelerate the training process using skip connections in bridging across convolutional and deconvolutional layers. The proposed model reaches on average 38.38 of PSNR and 0.98 of SSIM on a test set of 57458 images overcoming state-of-the-art models in the same application domain.Clinical relevance - Encoder-decoder based denoiser enables industry experts to provide more accurate and reliable medical interpretation and diagnosis in a variety of fields, from microscopy to surgery, with the benefit of real-time processing.