Described herein are means for implementing contrastive learning via reconstruction within a self-supervised learning framework, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for performing a random cropping operation to crop a 3D cube from each of a plurality of medical images received at the system as input; performing a resize operation of the cropped 3D cubes; performing an image reconstruction operation of the resized and cropped 3D cubes to predict the whole image represented by the original medical images received; and generating a reconstructed image which is analyzed for reconstruction loss against the original image representing a known ground truth image to the reconstruction loss function. Other related embodiments are disclosed.

Method and processes for segmentation of lungs lobes from CT image data are disclosed. Lobe segmentation relies on other segmentations, including the lungs, the lung airways and vasculature. Segmentation methods and processes begin with the acquisition of 3-D image data such as from a high resolution CT scan of a patient's lungs or at least a region thereof. The image is then processed to provide a segmentation mask from which the components are extracted to identify the lungs and airway. Lung vasculature and lobes are identified by similar processes.

A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

To improve the performance and accuracy of an image segmentation neural network, a cascaded robust learning framework for the segmentation of noisy labeled images includes two stages: a sample selection stage, and a joint optimization stage with label correction. In the first stage, the clean annotated samples are selected for network updating, so that the influence of noisy sample can be interactively eliminated. In the second stage, the label correction module works together with the joint optimization scheme to revise the imperfect labels. Thus, the training of the whole network is supervised by the corrected labels and the original ones.

Disclosed is a method of predicting a prognosis of a patient with adenocarcinoma using image features. The method of predicting a prognosis according to an embodiment of the present invention includes receiving an image including a lesion region of a patient, preprocessing the received image, segmenting the lesion region in the preprocessed image and calculating at least one of biomarkers indicating an intensity value and a texture information value within the segmented lesion region, and outputting a prognosis prediction value of the patient on the basis of the calculated at least one biomarker.

Systems and techniques that facilitate an augmented inspector interface with targeted, context-driven algorithms are provided. In various embodiments, a magnification component can magnify a portion of a medical image. In various embodiments, a recognition component can recognize an anatomical structure depicted in the portion of the medical image. In various embodiments, a recommendation component can recommend one or more sets of computing algorithms or computing operations related to the anatomical structure. In various embodiments, a menu component can display the one or more recommended sets of computing algorithms or computing operations in a drop-down menu.

A multi-label generating system is configured to: store a first plurality of medical scans with corresponding global labels and a second plurality of medical scans with corresponding region labels, wherein the global labels each correspond to one of a set of abnormality classes and wherein each of the region labels correspond to one of the set of abnormality classes; generate a computer vision model by training on the first plurality of medical scans with the corresponding global labels and the second plurality of medical scans with the corresponding region labels; receive a new medical scan; generate global probability data based on the computer vision model, wherein the global probability data indicates a set of global probability values corresponding to the set of abnormality classes, and wherein each of the set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in the new medical scan; and transmit the global probability data to a client device for display via a display device.

A method for analyzing an anatomical structure of a patient may include the steps of receiving volumetric scan data representative of one or more features of an anatomical structure; mapping the features to a node tree diagram; and displaying the node tree diagram. The features can comprise branching points, pathways connecting the branching points, and location data of the branching points and pathways. The node tree diagram may comprise a plurality of nodes and branches representing the branching points and pathways in the anatomical structure, respectively. The plurality of nodes may comprise a root node representing a root branching point as well as additional nodes representing additional branching points. Additionally, the node tree diagram may comprise a first set of one or more regions, wherein each region encompasses a respective portion of the node tree diagram and is representative of a defined portion of the anatomical structure.

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display, and a third bus for directly transmitting the received real-time video to the output port.

Systems and methods for providing a novel framework to simulate the appearance of pathology on patients who otherwise lack that pathology. The systems and methods include a “simulator” that is a generative adversarial network (GAN). Rather than generating images from scratch, the systems and methods discussed herein simulate the addition of diseases-like appearance on existing scans of healthy patients. Focusing on simulating added abnormalities, as opposed to simulating an entire image, significantly reduces the difficulty of training GANs and produces results that more closely resemble actual, unmodified images. In at least some implementations, multiple GANs are used to simulate pathological tissues on scans of healthy patients to artificially increase the amount of available scans with abnormalities to address the issue of data imbalance with rare pathologies.