Techniques are described for closed surface fitting (CSF). Processing circuitry may determine a plurality of points on a shape, determine a contour, used to determine a shape of an anatomical object, from image information of one or more images of a patient, and determine corresponding points on the contour that correspond to the plurality of points on the shape based on at least one of respective normal vectors projected from points on the shape and normal vectors projected from points on the contour. The processing circuitry may generate a plurality of intermediate points between the points on the shape and the corresponding points on the contour, generate an intermediate shape based on plurality of intermediate points, and generate a mask used to determine the shape of the anatomical object based on the intermediate shape.

Disclosed is a large-scale crop phenology extraction method based on a shape model fitting method. The method comprises: acquiring a multi-year vegetation index time sequence curve in a localized geographic region; performing smooth fitting on the vegetation index time sequence curve by using a dual logistic function fitting means; establishing shape models by using reference curves and reference points of agrometeorological stations; performing shape model fitting by means of transformation; and obtaining a phenological period extraction value of the localized geographic region by means of calculation using the optimal scaling parameter. According to the present invention, macroscopic features of the curve are used, such that the influence of localized fluctuation and noise of the curve can be reduced, and a better extraction precision is obtained; and each phenological period of a crop can be extracted at the same time.

Automated and objective methods for quantifying a retinal characteristic include segmenting an optical coherence tomography retinal image into a plurality of layered retinal regions, and quantifying the retinal characteristic for each region as normalized to a range defined by the characteristic value in the vitreous region and in the retinal pigment epithelium region. Such methods are useful for detecting occult ocular pathology, diagnosing ocular pathology, reducing age-bias in OCT image analysis, and monitoring efficacy ocular/retinal disease therapies.

A method of detecting whether or not a body chamber has an abnormal structure or function including: (a) providing a stack of images as input to a system comprising one or more hardware processors configured to obtain a stack of medical images comprising at least a representation of the body chamber inside the patient; to obtain a region of interest using a convolutional network trained to locate the body chamber, wherein the region of interest corresponds to the body chamber from each of the medical images; and to infer a shape of the body chamber using a stacked auto-encoder (AE) network trained to delineate the body chamber, wherein the AE network segments the body chamber; (b) operating the system to detect the body chamber in the images using deep convolutional networks trained to locate the body chamber, to infer a shape of the body chamber using a stacked auto-encoder trained to delineate the body chamber, and to incorporate the inferred shape into a deformable model for segmentation; and (c) detecting whether or not the body chamber has an abnormal structure, wherein an abnormal structure is indicated by a body chamber clinical indicia that is different from a corresponding known standard clinical indicia for the body chamber.

The present disclosure relates to an image processing method. The method may include: obtaining image data; reconstructing an image based on the image data, the image including one or more first edges; obtaining a model, the model including one or more second edges corresponding to the one or more first edges; matching the model and the image; and adjusting the one or more second edges of the model based on the one or more first edges.

A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

In a method for generating a surrogate marker based on medical image data mapping an image region, the medical image data is detected using a first interface, a first subregion of the image region is selected by segmenting a first structure included in the image region, a first property of the first subregion is extracted, the surrogate marker is determined based on the first property, and the surrogate marker is provided using a second interface.

A surgical instrument navigation system and method of use is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

The invention provides a method for determining a confidence value for an image segmentation. The method includes obtaining an image, wherein the image comprises a view of an anatomical structure and a model of the anatomical structure is obtained, wherein the model comprises a plurality of nodes. The image is processed to generate a plurality of image segmentation outputs, wherein each image segmentation output comprises a set of values for the view, wherein each value of the set of values is associated with a node of the plurality of nodes of the model. For each node of the model, a confidence value is determined based on the plurality of values corresponding to the node. A confidence map of the anatomical structure is generated based on the confidence value of each node.