A method for refining label information, which is performed by at least one computing device is disclosed. The method includes acquiring a pathology slide image including a plurality of patches, inferring a plurality of label information items for the plurality of patches included in the acquired pathology slide image using a machine learning model, applying the inferred plurality of label information items to the pathology slide image, and providing the pathology slide image applied with the inferred plurality of label information items to an annotator terminal.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

Embodiments discussed herein facilitate segmentation of vascular plaque, training a deep learning model to segment vascular plaque, and/or informing clinical decision-making based on segmented vascular plaque. One example embodiment accessing vascular imaging data for a patient, wherein the vascular imaging data comprises a volume of interest; pre-process the vascular imaging data to generate pre-processed vascular imaging data; provide the pre-processed vascular imaging data to a deep learning model trained to segment a lumen and a vascular plaque; and obtain segmented vascular imaging data from the deep learning model, wherein the segmented vascular imaging data comprises a segmented lumen and a segmented vascular plaque in the volume of interest.

An inverse visualization of a time-resolved angiographic image data set of a vascular system of a patient that was recorded by a medical imager during the flow of a contrast medium through the vascular system is provided. The time-resolved angiographic image data set of the vascular system has a temporal sequence of frames of the vascular system corresponding to the contrast medium filling process. A data set from bolus arrival times for each pixel or voxel is determined. The bolus arrival time corresponds to the time in the temporal sequence at which a predetermined contrast enhancement due to the contrast medium filling first occurs. A data set of temporally inverted bolus arrival times with respect to the contrast medium filling is determined for each pixel or voxel, resulting in a temporally inverted sequence of frames with respect to the contrast medium filling. The time-resolved angiographic image data set in the temporally inverted sequence is visualized.

A method for providing a feedback on a surgical outcome by a computer includes dividing, by the computer, actual surgical data obtained in an actual surgical process into a plurality of detailed surgical operations to obtain actual surgical cue sheet data composed of the plurality of detailed surgical operations, obtaining, by the computer, reference cue sheet data about the actual surgery, and comparing, by the computer, the actual surgical cue sheet data with the reference cue sheet data, and providing, by the computer, the feedback based on the comparison result.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

A computer implemented method for generating a 3D printable model of a patient specific anatomic feature from 2D medical images is provided. A 3D image is automatically generated from a set of 2D medical images. A machine learning based image segmentation technique is used to segment the generated 3D image. A 3D printable model of the patient specific anatomic feature is created from the segmented 3D image.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

Methods and systems for rending medical images within a web browser application. The web browser application retrieves a worklist and automatically determines an image study from the worklist to be cached. The web browser application retrieves at least one medical image included in the image study. The web browser application creates a web database for storing the at least one medical image within the browser application. When a user selects a medical image for display within the web browser, the web browser application determines whether the medical image is stored in the web database. When the medical image is stored in the web database, the web browser application retrieves the medical image from the web database. When the medical image is not stored in the web database, the web browser application retrieves the medical image from a remote image repository.

Methods and systems for rending medical images within a web browser application. The web browser application retrieves a worklist and automatically determines an image study from the worklist to be cached. The web browser application retrieves at least one medical image included in the image study. The web browser application creates a web database for storing the at least one medical image within the browser application. When a user selects a medical image for display within the web browser, the web browser application determines whether the medical image is stored in the web database. When the medical image is stored in the web database, the web browser application retrieves the medical image from the web database. When the medical image is not stored in the web database, the web browser application retrieves the medical image from a remote image repository.