Gregory N. McKay

Bio: Gregory N. McKay is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Engineering & Computer science. The author has an hindex of 7, co-authored 21 publications receiving 222 citations.

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Abstract: Nuclei mymargin segmentation is a fundamental task for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuclei but the accuracy of convolutional neural networks (CNNs) depends on the volume and the quality of labeled histopathology data for training. In particular, conventional CNN-based approaches lack structured prediction capabilities, which are required to distinguish overlapping and clumped nuclei. Here, we present an approach to nuclei segmentation that overcomes these challenges by utilizing a conditional generative adversarial network (cGAN) trained with synthetic and real data. We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework. This synthetic data along with real histopathology data from six different organs are used to train a conditional GAN with spectral normalization and gradient penalty for nuclei segmentation. This adversarial regression framework enforces higher-order spacial-consistency when compared to conventional CNN models. We demonstrate that this nuclei segmentation approach generalizes across different organs, sites, patients and disease states, and outperforms conventional approaches, especially in isolating individual and overlapping nuclei.

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images

Abstract: Nuclei segmentation is a fundamental task that is critical for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Conventional vision-based methods for nuclei segmentation struggle in challenging cases and deep learning approaches have proven to be more robust and generalizable. However, CNNs require large amounts of labeled histopathology data. Moreover, conventional CNN-based approaches lack structured prediction capabilities which are required to distinguish overlapping and clumped nuclei. Here, we present an approach to nuclei segmentation that overcomes these challenges by utilizing a conditional generative adversarial network (cGAN) trained with synthetic and real data. We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework. This synthetic data along with real histopathology data from six different organs are used to train a conditional GAN with spectral normalization and gradient penalty for nuclei segmentation. This adversarial regression framework enforces higher order consistency when compared to conventional CNN models. We demonstrate that this nuclei segmentation approach generalizes across different organs, sites, patients and disease states, and outperforms conventional approaches, especially in isolating individual and overlapping nuclei.

Visualization of blood cell contrast in nailfold capillaries with high-speed reverse lens mobile phone microscopy.

Abstract: Quantification of optical absorption gaps in nailfold capillaries has recently shown promise as a non-invasive technique for neutropenia screening. Here we demonstrate a low-cost, portable attachment to a mobile phone that can resolve optical absorption gaps in nailfold capillaries using a reverse lens technique and oblique 520nm illumination. Resolution <4μm within a 1mm2 on-axis region is demonstrated, and wide field of view (3.5mm × 4.8mm) imaging is achieved with resolution <6μm in the periphery. Optical absorption gaps (OAGs) are visible in superficial capillary loops of a healthy human participant by an ∼8-fold difference in contrast-to-noise ratio with respect to red blood cell absorption contrast. High speed video capillaroscopy up to 240 frames per second (fps) is possible, though 60fps is sufficient to resolve an average frequency of 37 OAGs/minute passing through nailfold capillaries. The simplicity and portability of this technique may enable the development of an effective non-invasive tool for white blood cell screening in point-of-care and global health settings.

Imaging human blood cells in vivo with oblique back-illumination capillaroscopy

Abstract: We present a non-invasive, label-free method of imaging blood cells flowing through human capillaries in vivo using oblique back-illumination capillaroscopy (OBC). Green light illumination allows simultaneous phase and absorption contrast, enhancing the ability to distinguish red and white blood cells. Single-sided illumination through the objective lens enables 200 Hz imaging with close illumination-detection separation and a simplified setup. Phase contrast is optimized when the illumination axis is offset from the detection axis by approximately 225 µm when imaging ∼80 µm deep in phantoms and human ventral tongue. We demonstrate high-speed imaging of individual red blood cells, white blood cells with sub-cellular detail, and platelets flowing through capillaries and vessels in human tongue. A custom pneumatic cap placed over the objective lens stabilizes the field of view, enabling longitudinal imaging of a single capillary for up to seven minutes. We present high-quality images of blood cells in individuals with Fitzpatrick skin phototypes II, IV, and VI, showing that the technique is robust to high peripheral melanin concentration. The signal quality, speed, simplicity, and robustness of this approach underscores its potential for non-invasive blood cell counting.

MDS Clinical Diagnostic Criteria for Parkinson's Disease (S19.001)

Abstract: Objective To present the International Parkinson and Movement Disorder Society (MDS) Clinical Diagnostic Criteria for Parkinson9s disease. Background Although several diagnostic criteria for Parkinson9s disease have been proposed, none have been officially adopted by an official Parkinson society. Moreover, the commonest-used criteria, the UK brain bank, were created more than 25 years ago. In recognition of the lack of standard criteria, the MDS initiated a task force to design new diagnostic criteria for clinical Parkinson9s disease. Methods/Results The MDS-PD Criteria are intended for use in clinical research, but may also be used to guide clinical diagnosis. The benchmark is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise. Although motor abnormalities remain central, there is increasing recognition of non-motor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the MDS-PD Criteria retain motor parkinsonism as the core disease feature, defined as bradykinesia plus rest tremor and/or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies upon three categories of diagnostic features; absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of PD diagnosis). Two levels of certainty are delineated: Clinically-established PD (maximizing specificity at the expense of reduced sensitivity), and Probable PD (which balances sensitivity and specificity). Conclusion The MDS criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, criteria will need continuous revision to accommodate these advances. Disclosure: Dr. Postuma has received personal compensation for activities with Roche Diagnostics Corporation and Biotie Therapies. Dr. Berg has received research support from Michael J. Fox Foundation, the Bundesministerium fur Bildung und Forschung (BMBF), the German Parkinson Association and Novartis GmbH.

Deep learning for cellular image analysis

Abstract: Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.

Digital pathology and artificial intelligence

Abstract: In modern clinical practice, digital pathology has a crucial role and is increasingly a technological requirement in the scientific laboratory environment. The advent of whole-slide imaging, availability of faster networks, and cheaper storage solutions has made it easier for pathologists to manage digital slide images and share them for clinical use. In parallel, unprecedented advances in machine learning have enabled the synergy of artificial intelligence and digital pathology, which offers image-based diagnosis possibilities that were once limited only to radiology and cardiology. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond a microscopic slide and enable true utilisation and integration of knowledge that is beyond human limits and boundaries, and we believe there is clear potential for artificial intelligence breakthroughs in the pathology setting. In this Review, we discuss advancements in digital slide-based image diagnosis for cancer along with some challenges and opportunities for artificial intelligence in digital pathology.

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

Abstract: The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation and interpretability issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained attention multiple instance learning, an easy-to-use, high-throughput, and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning-based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and biopsies. CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.