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.

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.

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.

Some embodiments of this application disclose an image processing method, including: acquiring an image-to-be-processed, the image-to-be-processed including a target part of an object, the morphology of the target part being a first morphology, and the first morphology being not matched with an expected morphology; acquiring description information corresponding to the first morphology, and acquiring contour information of the target part in the first morphology; and correcting the morphology of the target part based on the description information and the contour information so as to correct the morphology of the target part from the first morphology to a second morphology, the second morphology being matched with the expected morphology. By adopting some embodiments of the disclosure, manpower resources can be saved, and the morphology correction efficiency is improved.

A three-dimensional shape of a subject is analyzed by inputting captured images of a depth camera and a visible light camera. There is provided an image processing unit configured to input captured images of the depth camera and the visible light camera, to analyze a three-dimensional shape of the subject. The image processing unit generates a depth map based TSDF space (TSDF Volume) by using a depth map acquired from a captured image of the depth camera, and generates a visible light image based TSDF space by using a captured image of the visible light camera. Moreover, an integrated TSDF space is generated by integration processing on the depth map based TSDF space and the visible light image based TSDF space, and three-dimensional shape analysis processing on the subject is executed using the integrated TSDF space.

An image segmentation method, apparatus, device, and a medium that includes: performing a first segmentation on a target grayscale image to obtain a first sub-image set, where the target image is a grayscale image of a target color image; determining, using a grayscale histogram of each first sub-image in the first sub-image set, corresponding target grayscale data, including a mean, maximum, and minimum grayscale value; determining, using the target grayscale data, whether the first sub-image satisfies a preset segmentation condition; if so, performing a second segmentation on the first sub-image to obtain a second sub-image set; performing, using an OTSU maximum inter-class variance method, binarization processing respectively on each second sub-image in the second sub-image set and each first sub-image not subjected to the second segmentation, to obtain a corresponding first binarized image; and performing, using the first binarized image, watershed segmentation to obtain a segmented image.

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.

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.

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.

Provided is a method for bone suppression in an X-ray image, which includes: extracting an upper contour line and a lower contour line corresponding to a bone to be suppressed from the original X-ray image; generating a first binarization image and a second binarization image based on the upper contour line and the lower contour line, respectively; generating a first distance transform image and a second distance transform image from the first binarization image and the second binarization image, respectively through distance transform; generating a compensated first X-ray image and a compensated second X-ray image by compensating a pixel value of a region which belongs to the bone by using the first distance transform image and the second distance transform image, respectively from the original X-ray image; and synthesizing the compensated first X-ray image and the compensated second X-ray image to obtain a bone-suppressed X-ray image.