Automated 3D Labelling of Fibroblasts and Endothelial Cells in SEM-Imaged Placenta using Deep Learning

Abstract

Analysis of fibroblasts within placenta is necessary for research into placental growth-factors, which are linked to lifelong health and chronic disease risk. 2D analysis of fibroblasts can be challenging due to the variation and complexity of their structure. 3D imaging can provide important visualisation, but the images produced are extremely labour intensive to construct because of the extensive manual processing required. Machine learning can be used to automate the labelling process for faster 3D analysis. Here, a deep neural network is trained to label a fibroblast from serial block face scanning electron microscopy (SBFSEM) placental imaging.

The future of bone regeneration: integrating AI into tissue engineering

“…With relatively little data processing required, and the ability to extract features and characteristics without prior knowledge of biology, chemistry or medicine, advances can be made in areas where physicians currently struggle, such as determining if novel variants in a patient's genome are medically relevant, whether abnormalities in plasma could be early-onset cancerous mutations, or how cell features are linked to tumour pathology [89,90]. Medical imaging is often at the forefront in integrating AI, as it is a field of complex and ambiguous data, which often requires lengthy manual processing, where an expert will medically interpret and analyse, and then annotate, large amounts of data [91,92]. While ConvNets and deep learning have traditionally been utilised effectively in segmentation of tissues and organs, increasingly more complex tasks are being advanced through deep learning, such as detection of abnormalities or non-invasive cell counting, morphological identification and behavioural prediction, including with stem cells [35,87,[93][94][95][96][97][98][99].…”

“…Once the data type has been obtained, and the task ascertained, the next step is to acquire an appropriate network architecture. The methodology for using deep learning to predict adult skeletal stem cell response to micro-patterned topographies [35] is the exact same method as used in the 3D labelling of cells in placenta with nanoscale resolution [92], and in the super-resolution transformation of 20× optical microscope images into 1500× SEM images [131]. In total, the architecture used (the previously mentioned open-source pix2pix [129]) has been cited almost seven thousand times, implying it has been used for hundreds, if not thousands, of different purposes in dozens of varied fields.…”

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning

“…The deep neural network architecture consisted of both a generator and a discriminator network [33], where the former follows a W-Net architecture [34] (Fig. 3), and the latter was a convolutional network without deconvolution.…”