University of South Florida

Stereology Resource Center, Inc.

H. Lee Moffitt Cancer Center and Research Institute, Inc.

Dmitry B Goldgof

Lawrence O Hall

Peter Randolph Mouton

Robert J Gillies

Yu Sun

Cagri Cetin

Ghadh A Alzamzmi

Rangachar Kasturi

Terri Ashmeade

Baishali Chaudhury

Jarred Adam Ligatti

Rahul Paul

Jay Ligatti

Hady Ahmady Phoulady

Shamaria Engram

Jean-Baptiste Subils

Md Sirajus Salekin

Sudeep Sarkar

Robert A Gatenby

Yoganand Balagurunathan

Olya Stringfield

Thao Ho

Sammie Lee Elkins

Leonid V Tsap

Palak Pankajbhai Dave

Om Pavithra Bonam

Daniel T Elozory

Kurt A Kramer

John Arrington

Natarajan Raghunand

Chih-Yun Pai

Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.

Systems and methods for determining cell number count in automated stereology z-stack images

A Neonatal CNN (N-CNN) is provided for detecting neonatal pain emotion based upon facial recognition. A cascaded N-CNN is trained using a Neonatal Pain Assessment Database (NPAD) to automatically identify a neonatal patient experience pain in real-time. These results show that the automatic recognition of neonatal pain provided by the embodiments of the present invention is a viable and more efficient alternative to the current standard of pain assessment.

Neonatal pain identification from neonatal facial expressions

Systems and methods for automated stereology are provided. A method can include providing an imager for capturing a Z-stack of images of a three-dimensional (3D) object; constructing extended depth of field (EDF) images from the Z-stack of images; performing a segmentation method on the EDF images including estimating a Gaussian Mixture Model (GMM), performing morphological operations, performing watershed segmentation, constructing Voronoi diagrams and performing boundary smoothing; and determining one or more stereology parameters such as number of cells in a region.

Automated stereology for determining tissue characteristics

The present disclosure describes a multi-initialization ensemble-based defense strategy against an adversarial attack. In one embodiment, an exemplary method includes training a plurality of conventional neural networks (CNNs) with a training set of images, wherein the images include original images and images modified by an adversarial attack; after training of the plurality of conventional neural networks, providing an input image to the plurality of conventional neural networks, wherein the input image has been modified by an adversarial attack; receiving a probability output for the input image from each of the plurality of conventional neural networks; producing an ensemble probability output for the input image by combining the probability outputs from each of the plurality of conventional neural networks; and labeling the input image as belonging to one of the one or more categories based on the ensemble probability output.

Mitigating adversarial attacks on medical imaging understanding systems

A computer-based system and method for generating a current pain assessment of a neonate using facial expressions along with crying sounds, body movement, and vital signs changes and for using the current pain objective assessment to predict future pain objective assessment and assign a future pain probability score by incorporation spatiotemporal data into the multimodal assessment.

System and method for multimodal spatiotemporal pain assessment

A system and method of automatically assessing pediatric and neonatal pain using facial expressions along with crying sounds, body movement, and vital signs change to improve the diagnosis and treatment of pain in the pediatric patient population.

Comprehensive and context-sensitive neonatal pain assessment system and methods using multiple modalities

A system and method for measuring an infant's pain intensity is presented. The method for assessing an infant's pain intensity based on facial expressions is comprised of three main stages: detection of an infant's face in video sequence followed by preprocessing operations including face alignment; expression segmentation; and expression recognition or classification. Also presented is a multimodal system for assessing an infant's pain intensity using the following classifiers: facial expression classifier; vital sign classifier; crying recognition classifier; body motion classifier and state of arousal classifier. Each classifier generates an individual score, all of which are normalized and weighed to generate a total pain score that indicates pain intensity.

Machine-based infants pain assessment tool

Virtually every cancer patient is imaged with CT, PET or MRI. Importantly, such imaging reveals that tumors are complex and heterogeneous, often containing multiple habitats within them. Disclosed herein are methods for analyzing these images to infer cellular and molecular structure in each of these habitats. The methods can involve spatially superimposing two or more radiological images of the tumor sufficient to define regional habitat variations in two or more ecological dynamics in the tumor, and comparing the habitat variations to one or more controls to predict the severity of the tumor.

Radiologically identified tumor habitats

Systems and methods for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.

System and method for automated stereology of cancer

An example method for diagnosing tumors in a subject by performing a quantitative analysis of a radiological image can include identifying a region of interest (ROI) in the radiological image, segmenting the ROI from the radiological image, identifying a tumor object in the segmented ROI and segmenting the tumor object from the segmented ROI. The method can also include extracting a plurality of quantitative features describing the segmented tumor object, and classifying the tumor object based on the extracted quantitative features. The quantitative features can include one or more texture-based features.

Systems and methods for diagnosing tumors in a subject by performing a quantitative analysis of texture-based features of a tumor object in a radiological image

A system for generating symmetric cryptographic keys for communications between hosts. Hosts use associated devices to generate secret keys. Each key is generated based on a static seed and a dynamic seed. The dynamic seed is created from sensor data or auxiliary data. The secret key allows host machines to encrypt, or decrypt, plaintext messages sent to, or received from, other host machines.

Systems and methods for generating symmetric cryptographic keys

This invention relates to a system and method for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.

Growing community of inventors

Tampa, FL, United States of America

Dmitry B Goldgof