A method, system and computer programs for computing simultaneous rectilinear paths using medical images are disclosed. The method comprises receiving a 3D medical image comprising voxels representing a volume of an anatomical region of a patient and a preliminary path determined by two points traversing said 3D medical image, wherein said 3D medical image has segmented therein at least one area of interest, the preliminary path comprising a security zone with a given distance; computing a distance map of said area of interest and mapping its voxels to a first value or to a second value depending on a distance threshold, the latter being equal to said given distance of the security zone; selecting the voxels having said second value and projecting them using a frustum that projects the preliminary path onto a single point, to obtain a 2D projected image that includes a plurality of rectilinear paths.

Presented herein are systems and methods that allow for vertebral centrums of individual vertebrae to be identified and segmented within a 3D image of a subject (e.g., a CT or microCT image). In certain embodiments, the approaches described herein identify, within a graphical representation of an individual vertebra in a 3D image of a subject, multiple discrete and differentiable regions, one of which corresponds to a vertebral centrum of the individual vertebra. The region corresponding to the vertebral centrum may be automatically or manually (e.g., via a user interaction) classified as such. Identifying vertebral centrums in this manner facilitates streamlined quantitative analysis of 3D images for osteological research, notably, providing a basis for rapid and consistent evaluation of vertebral centrum morphometric attributes.

The method for determining property feature segmentation includes: receiving a region image for a region; determining parcel data for the region; determining a final segmentation output based on the region image and parcel data using a trained segmentation module; optionally generating training data; and training a segmentation module using the training data S500.

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation via multiple regression layers, implementing instance segmentation based on partial annotations, and/or implementing user interface configured to facilitate user annotation for instance segmentation. In various embodiments, a computing system might generate a user interface configured to collect training data for predicting instance segmentation within biological samples, and might display, within a display portion of the user interface, the first image comprising a field of view of a biological sample. The computing system might receive, from a user via the user interface, first user input indicating a centroid for each of a first plurality of objects of interest and second user input indicating a border around each of the first plurality of objects of interest. The computing system might train an AI system to predict instance segmentation of objects of interest in images of biological samples.

Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes detecting at least one bifurcation of the object using a trained bifurcation learning network based on the image. The method further includes extracting the centerline of the object based on a constraint condition that the centerline passes through the detected bifurcation.

An image processing apparatus includes a generation unit configured to generate a first distance image having a pixel value based on a distance from an outline of a region that indicates a predetermined part of the subject and is extracted from a first image, and having a resolution lower than a resolution of the first image, and generate a second distance image having a pixel value based on a distance from an outline of a region that indicates the predetermined part and is extracted from a second image, and having a resolution lower than a resolution of the second image, a first calculation unit configured to calculate first deformation information by registering the first distance image and the second distance image, and a second calculation unit configured to calculate second deformation information by registering the first image and the second image based on the first deformation information.

Systems and methods for generating a centerline for an object in an image are provided. An exemplary method includes receiving an image containing the object. The method also includes generating a distance cost image using a trained first learning network based on the image. The method further includes detecting end points of the object using a trained second learning network based on the image. Moreover, the method includes extracting the centerline of the object based on the distance cost image and the end points of the object.

A method for performing a computer-aided diagnosis (CAD) includes: acquiring a medical image set; generating a three-dimensional (3D) tumor distance map corresponding to the medical image set, each voxel of the tumor distance map representing a distance from the voxel to a nearest boundary of a primary tumor present in the medical image set; and performing neural-network processing of the medical image set to generate a predicted probability map to predict presence and locations of oncology significant lymph nodes (OSLNs) in the medical image set, wherein voxels in the medical image set are stratified and processed according to the tumor distance map.

One example provides a computing system comprising a depth sensor comprising a plurality of pixels, and a storage machine holding instructions executable by a logic machine to, for each pixel, make K phase measurements to form a set of noisy phase measurements, determine a location at which a projection line that passes through the set of noisy phase measurements in a K-dimensional phase space passes through a lower dimensional plane, the projection line being parallel to a noise free phase evolution line, compare the location to a plurality of independent terms of a predetermined matrix of points in the lower dimensional plane, locate a corresponding set of noiseless phase orders by using a selected set of independent terms to reference a look-up table, determine a distance value for the pixel based upon the corresponding set of noiseless phase orders, and output the distance value for the pixel.

The disclosure relates to a method for determining a boundary about an area of interest in an image set. The includes obtaining the image set from an imaging modality and processing the image set in a convolutional neural network. The convolutional neural network is trained to perform the acts of predicting an inverse distance map for the actual boundary in the image set; and deriving the boundary from the inverse distance map. The disclosure also relates to a method of training a convolutional neural network for use in such a method, and a medical imaging arrangement.