Abstract: A method for segmenting metal objects in projection images acquired using different projection geometries is provided. Each projection image shows a region of interest. A three-dimensional x-ray image is reconstructed from the projection images in the region of interest. A trained artificial intelligence segmentation algorithm is used to calculate first binary metal masks for each projection image. A three-dimensional intermediate data set of a reconstruction region that is larger than the region of interest is reconstructed by determining, for each voxel of the intermediate data set, as a metal value, a number of first binary metal masks showing metal in a pixel associated with a ray crossing the voxel. A three-dimensional binary metal mask is determined. Second binary metal masks are determined for each projection image by forward projecting the three-dimensional binary metal mask using the respective projection geometries.

Abstract: Systems and methods are provided for provided for automatic evaluation of a human embryo. An image of the embryo is obtained and provided to a neural network to generate a plurality of values representing the morphology of the embryo. The plurality of values representing the morphology of the embryo are evaluated at an expert system to provide an output class representing one of a current quality of the embryo, a future quality of the embryo, a likelihood that implantation of the embryo will be successful, and a likelihood that implantation of the embryo will result in a live birth.

Abstract: An image analysis device may obtain target image data representing a target image which is an analysis target, specify (m×n) partial images sequentially by scanning the target image data, wherein the (m×n) partial images are constituted of m partial images aligned along a first direction and n partial images aligned along a second direction, generate first probability data by using the (m×n) partial images and the first object data in the memory, and reduce the target image data so as to generate reduced image data. The image analysis device may execute image analysis according to a convolutional neural network by using the reduced image data as K pieces of channel data corresponding to K channels and using the first probability data as one piece of channel data corresponding to one channel, and output a result of the image analysis.

Abstract: A framework for automatically finding an organ in image data. In accordance with one aspect, a predetermined view of the organ is approximated by transforming the original image data to generate transformed image data. A best-match region in the transformed image data that best matches a synthesized geometric shape may then be found. The best-match region may be transformed into a volume space of the original image data to generate a location of the organ.

Abstract: A system includes a structural imaging acquisition unit, a functional imaging acquisition unit, and one or more processors. The structural imaging acquisition unit is configured to perform a structural scan to acquire structural imaging information of a patient. The functional imaging acquisition unit is configured to perform a functional scan to acquire functional imaging information of a patient.

Abstract: Systems and methods for a reduced interaction CT scanning workflow. A sensor is used to capture an image of a patient on the table. Scan parameters are automatically set. A full CT scan is performed without a scout scan. During the full CT scan, the scan parameters are adjusted based on the raw CT measurements from the full CT scan. A radiology report is automatically generated from the results of the full CT scan.

Abstract: An image processing methods includes: obtaining a first image, identifying a target object in the first image, and obtaining body detection information of the target object; obtaining first detection information related to a leg region of the target object in the body detection information; and performing image deformation processing on the leg region corresponding to the first detection information to generate a second image.

Abstract: Medical images may be classified by receiving a first medical image. The medical image may be applied to a machine learned classifier. The machine learned classifier may be trained on second medical images. A label of the medical image and a measure of uncertainty may be generated. The measure of uncertainty may be compared to a threshold. The first medical image and the label may be output when the measure of uncertainty is within the threshold.

Abstract: A method and apparatus include performing a first image analysis at a first resolution of an input tissue image. An uncertainty map is generated based on performing the first image analysis. A set of uncertain regions of the input tissue image are identified based on the uncertainty map. A second image analysis of the set of uncertain regions is performed at a second resolution of the tissue image that is greater than the first resolution. An analysis result is generated based on the first image analysis and the second image analysis.

Abstract: A method and system for preprocessing microscopic images. A method includes identifying a plurality of microglial cells shown in microglial cell channel images, wherein identifying the plurality of microglial cells further comprises quantizing the microglial cell channel microscopic images and filtering a plurality of objects in the quantized microglial cell channel microscopic images; identifying a soma and at least one projection for each of the plurality of microglial cells, wherein identifying the soma and the at least one projection for each microglial cell further includes iteratively removing pixels on the microglial cell as shown in one of the microglial cell channel images, wherein each soma and each projection has a respective size; and determining an activation state of each microglial cell based on the size of the soma and a total size of the at least one projection identified for the microglial cell.

Abstract: A medical imaging system for acquiring medical image data from an imaging zone. The medical imaging system includes a memory for storing machine executable instructions and medical imaging system commands. The medical imaging system further includes a user interface and a processor. Execution of the machine executable instructions causes the processor to: receive scan parameter data for modifying the behavior of the medical imaging system commands; receive metadata descriptive of imaging conditions from the user interface; store configuration data descriptive of a current configuration of the medical imaging system in the memory; calculate an error probability by comparing the metadata, the configuration data, and the scan parameter data using a predefined model, wherein the error probability is descriptive of a deviation between the metadata and between the configuration data and/or the scan parameter data; perform predefined action if the error probability is above a predetermined threshold.

Abstract: A system and method for creating and using a medical imaging bioinformatics annotated (“MIBA”) master file is disclosed. Creating the MIBA master file includes receiving image data, performing a first registration on the image data for obtaining in-slice registered data, and performing a second registration for registering the in-slice registered data to a three-dimensional (3D) model for obtaining source data. Creating also includes extracting voxel data from the source data and storing the voxel data in a MIBA database, receiving selection of a volume of interest, and extracting a portion of the voxel data corresponding to the volume of interest. The MIBA master file is created from the portion of the voxel data, which is stored in the MIBA database. The MIBA system receives a query, extracts data from the MIBA master file in response to the query, and presents the extracted data on an output interface.

Abstract: Presented herein are efficient and reliable systems and methods for calculating and extracting three-dimensional central axes of bones of animal subjects—for example, animal subjects scanned by in vivo or ex vivo microCT platforms—to capture both the general and localized tangential directions of the bone, along with its shape, form, curvature, and orientation. With bone detection and segmentation algorithms, the skeletal bones of animal subjects scanned by CT or microCT scanners can be detected, segmented, and visualized. Three dimensional central axes determined using these methods provide important information about the skeletal bones.

Abstract: A medical image processing device having a processor configured to: acquire a medical image including an image of a subject; perform a first recognition of the medical image using a first recognizer; determine a confidence level for a recognition result of a first recognition by the first recognition; and perform a second recognition of the medical image using a second recognizer according to the confidence level for the recognition result of the first recognition, the second recognition having higher recognition accuracy than the first recognition.

Abstract: An ultrasound diagnostic apparatus according to an embodiment includes a processing circuitry. The processing circuitry acquires first image data that is image data obtained during the ultrasound scan executed on the object and that is image data before the coordinate conversion corresponding to the format of the ultrasound scan. The processing circuitry uses the trained model generated through the learning using the first image data obtained during the previously executed ultrasound scan and the area including the object in the first image data to estimate the area in the acquired first image data.

Abstract: Methods and systems are provided for adaptive scan control. In one embodiment, a method includes, upon an injection of a contrast agent, performing a plurality of perfusion acquisitions of a first anatomical region of interest (ROI) of a subject with the imaging system, processing projection data of the first anatomical ROI obtained from the plurality of perfusion acquisitions to measure a contrast signal of the contrast agent, performing a plurality of angiography acquisitions, each angiography acquisition performed at a respective time determined based on the contrast signal, and performing one or more additional perfusion acquisitions between each angiography acquisition.

Abstract: An image analysis device may include a memory storing learning data for executing image analysis, and may obtain cell image data representing a cell image including a plurality of cell objects, sequentially identify plural pieces of partial image data from the cell image data, sequentially execute a center determination process on each of the plural pieces of partial image data, classify at least one cell corresponding to at least one cell object among a plurality of cell objects by using results of the center determination process on the plural pieces of partial image data and classification data included in learning data, and output a classification result.

Abstract: A cell visualization system includes a digital holographic microscopy (DHM) device, a training device, and a virtual staining device. The DHM device produces DHM images of cells and the virtual staining device colorizes the DHM images based on an algorithm generated by the training device using generative adversarial networks and unpaired training data. A computer-implemented method for producing a virtually stained DHM image includes acquiring an image conversion algorithm which was trained using the generative adversarial networks, receiving a DHM image with depictions of one or more cells and virtually staining the DHM image by processing the DHM image using the image conversion algorithm. The virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell.

Abstract: An embodiment of the invention relates to a method for calculating an image matrix size N for reconstructing image data of an examination subject from projection data. The method includes acquiring projection data obtained during a relative rotational movement between a radiation source of a computed tomography system and the examination subject; calculating the image matrix size N as a function of an extent of an axial field of view of the computed tomography system and a sharpness value in the image data to be reconstructed; and making the calculated image matrix size available to a reconstruction unit to reconstruct the image data from the projection data acquired.