Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

Clymer, Daniel; Kostadinov, Stefan; Catov, Janet M.; Skvarca, Lauren; Pantanowitz, Liron; Cagan, Jonathan; LeDuc, Philip R.

doi:10.1016/j.ajpath.2020.06.014

Cited by 25 publications

(17 citation statements)

References 34 publications

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“…In addition, the transparency and biocompatibility of the LEPD substrate allow for imaging-based assays followed by their analysis using our AI pipeline. This framework can potentially be useful for high-throughput arrayed screening studies (13,(70)(71)(72), where cells in each LEPD well are transfected with a different cargo (e.g., different siRNAs and Cas9/sgRNA RNPs) to perturb a single gene followed by live-cell tracking (e.g., to study morphology, proliferation, and cell-cell interaction) and end-point high-content imaging (e.g., to look at protein expression) to analyze cell phenotype and identify the top genetic targets.…”

Section: Discussionmentioning

confidence: 99%

Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering

et al. 2022

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Manipulation of cells for applications such as biomanufacturing and cell-based therapeutics involves introducing biomolecular cargoes into cells. However, successful delivery is a function of multiple experimental factors requiring several rounds of optimization. Here, we present a high-throughput multiwell-format localized electroporation device (LEPD) assisted by deep learning image analysis that enables quick optimization of experimental factors for efficient delivery. We showcase the versatility of the LEPD platform by successfully delivering biomolecules into different types of adherent and suspension cells. We also demonstrate multicargo delivery with tight dosage distribution and precise ratiometric control. Furthermore, we used the platform to achieve functional gene knockdown in human induced pluripotent stem cells and used the deep learning framework to analyze protein expression along with changes in cell morphology. Overall, we present a workflow that enables combinatorial experiments and rapid analysis for the optimization of intracellular delivery protocols required for genetic manipulation.

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Section: Discussionmentioning

confidence: 99%

Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering

et al. 2022

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“…In order to further optimize the diagnostic process, deep learning models could assist the pathologists and Mohs surgeons to rapidly and precisely detect tumor ROI [ 62 , 63 ]. In the field of conventional pathology, various applications incorporating machine learning algorithms have generally shown a satisfactory performance to detect cell nuclei [ 64 ], mitosis [ 65 ], glands [ 8 ] and blood vessels [ 66 ]. Moreover, machine-learning algorithms have the capability to extract subtle features in acquired images beyond what is perceived by the human eye [ 67 ], and in turn identify patterns and associations [ 68 , 69 , 70 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

Ruini

Schlingmann

Jonke³

et al. 2021

Cancers

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Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

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“…Chadaj et al [72] tackled the problem of differentiating blood vessels (informative) from hemorrhage (uninformative) using the CMYK color-space and mathematical morphology to feed a decision tree. Blood detection is also a critical step in the diagnosis pipeline presented by Clymer et al [73], where a RetinaNet model is used to detect blood vessels at low resolution, which were subsequently classified using an Xception CNN.…”

Section: Damaged and Blood Areasmentioning

confidence: 99%

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

et al. 2022

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Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color processing techniques are usually the main concern, while other areas are frequently ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.

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Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

Cited by 25 publications

References 34 publications

Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering

Multiplexed high-throughput localized electroporation workflow with deep learning–based analysis for cell engineering

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

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