2 Deep learning-based cellular image analysis for intelligent medical diagnosis

Advances in microscopy have been revealing the mysteries in biological sciences since the first microorganism was observed with a simple compound microscope in the seventeenth century, which led to an understanding of the origin of diseases and the development of treatments for human longevity. Since then progress in optics was transformative for gathering a wide range of information regarding the molecular infrastructure of the cell. Thus, more sophisticated instruments have been developing for the investigation of biological entities with different aspects. Morphological and functional heterogeneity of human cells is the greatest challenge for detection in biomedical sciences. Moreover, the quantification of the acquired image data is critical for clinical translation. Conventional imageprocessing algorithms require a series of strict rules regarding the data of interest (e.g., segmentationof single cells in 2D cell culture) and a high level of user interpretation that tunes the parameters to obtain satisfactory results. This procedure is labor-intensive although it performs relatively well for routine tasks. However, research and development-oriented image processing of cells cannot be led to a categorization based on a single range of visual parameters (e.g., circularity or intensity of blood cells). Thus, in the last decade, software tools that learn from the data itself have gained great attention in biological sciences. Although classical machine learning models have a certain level of success in these cases, the major problem is that features are still needed to be designed prior to the specific application. Since a model inspired by deep-learning architecture obtained unprecedented success in the 2012 ImageNet Large Scale Visual Recognition Challenge, several applications arose for medical applications due to its comparable ability of learning and inferring at the human level. Thus, a method called transfer learning, which was trained for recognizing low features of objects within an image (e.g., object edges), has been used for training special medical datasets for diagnosis. There are four main fields in cellular imaging for medical diagnostics that might effectively benefit from deep learning: (i) image classification based on the features of a group of cells for clinical diagnostics (e.g., malign or benign cell detection), (ii) image segmentation partitions of an image for grouping each object presented (e.g., identifying cells in an image), (iii) visualization of changes regarding a cell or group of cells at limited time scale (e.g., bacterial cell growth or motility of embryo) by object tracking for cellular image analysis and (iv) fusing different imaging modality to reveal hidden information in each domain such as brightfield microscopy, fluorescence microscopy and digital holographic microscopy. This chapter introduces the latest achievements using deep-learning-based image processing techniques for identification and/or sorting of cell of interest that could be potentially applied in clinics for diagnosis and treatment. These techniques became accurate, sensitive, rapid and cost-effective over time so therefore it is expected that they will be translated to assist medical professionals and improve healthcare outcomes in the future.