Techniques are provided for determining a cell count within a whole slide pathology image. The image is segmented using a global threshold value to define a tissue area. A plurality of patches comprising the tissue area are selected. Stain intensity vectors are determined within the plurality of patches to generate a stain intensity image. The stain intensity image is iteratively segmented to generate a cell mask using a local threshold value that is and gradually reduced after each iteration. A chamfer distance transform is applied to the cell mask to generate a distance map. Cell seeds are determined on the distance map. Cell segments are determined using a watershed transformation, and a whole cell count is calculated for the plurality of patches based on the cell segments. A client device may be configured for real-time cell counting based on the whole cell count.

A computer implemented method using artificial intelligence for matching images with locations and directions by acquiring a plurality of panoramic images, detecting objects and their locations in each of the panoramic images, acquiring a floorplan image, detecting objects and their locations in the floorplan image, comparing the objects and locations detect in each of the panoramic image to the objects and locations detected in the floorplan image, and determining a location in the floorplan image where each panoramic image was taken.

Novel tools and techniques are provided for implementing digital microscopy imaging using deep learning-based segmentation and/or implementing instance segmentation based on partial annotations. In various embodiments, a computing system might receive first and second images, the first image comprising a field of view of a biological sample, while the second image comprises labeling of objects of interest in the biological sample. The computing system might encode, using an encoder, the second image to generate third and fourth encoded images (different from each other) that comprise proximity scores or maps. The computing system might train an AI system to predict objects of interest based at least in part on the third and fourth encoded images. The computing system might generate (using regression) and decode (using a decoder) two or more images based on a new image of a biological sample to predict labeling of objects in the new image.

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.

An image processing apparatus includes a first extraction unit configured to extract a first target region from an image using a trained classifier, a setting unit configured to set region information to be used in a graph cut segmentation method based on a first extraction result including the first target region, a second extraction unit configured to extract a second target region using the graph cut segmentation method based on the set region information, and a generation unit configured to generate a ground truth image corresponding to the image based on a second extraction result including the second target region.

A method of image-distance transformation using bi-directional scans is provided. The method includes the steps of: performing a first scan on each pixel of an input image using a first mask in a first order to generate an intermediate image; and performing a second scan on each pixel of the intermediate image using a second mask in a second order to obtain distance information of each pixel in the input image. A first current pixel in the input image that is not compared with prior pixels in the first order and in a first current segment is used in the first comparison process in the first scan, and a second current pixel that is compared with prior pixels in the second order and in a second segment is used in the second comparison process in the second scan.

Methods and apparatuses in which a plurality of images are recorded at different illumination angles are provided. The plurality of images are combined in order to produce a results image with an increased depth of field.

Techniques related to performing image registration are discussed. Such techniques include converting a source image region and a target image portion from a color image space to a semantic space and iteratively converging homography parameters using the source image region and target image portion in the semantic space by applying iterations with some homography parameters allowed to vary and others blocked from varying and subsequent iterations with all homography parameters allowed to vary.

A computer system is provided that includes a camera device and a processor configured to receive scene data captured by the camera device for a three-dimensional environment that includes one or more physical objects, generate a geometric representation of the scene data, process the scene data using an artificial intelligence machine learning model that outputs object boundary data and object labels, augment the geometric representation with the object boundary data and the object labels, and identify the one or more physical objects based on the augmented geometric representation of the three-dimensional environment. For each identified physical object, the processor is configured to generate an associated virtual object that is fit to one or more geometric characteristics of that identified physical object. The processor is further configured to track each identified physical object and associated virtual object across successive updates to the scene data.

An information processing apparatus includes: a memory; and a processor coupled to the memory and configured to: partition pixel values in a unit of row of an input image into a plurality of sections and allocates threads to the respective sections of the row, the threads being enabled to run in parallel by a processor; calculate, with each of the threads allocated in each row, distances each from a pixel having a certain value in the corresponding section of the row in the input image, and generates a first distance image which stores values indicating the distances; and calculate, with each of the threads allocated in each row, a first boundary value indicating a distance from a pixel having the certain value in another section of each row, by using a calculation result of the first boundary value in the another section of each row.