Abstract: Systems and methods for diagnosing autism spectrum disorder (ASD) using only functional magnetic resonance imaging (fMRI) data are provided. Machine learning infrastructure can be used to identify reliable biomarkers of ASD in order to classify patients with ASD from among a group of typical control subjects using only fMRI. A sparse autoencoder (SAE) can be used, resulting in optimized extraction of features that can be used for classification. These features can then be fed into a deep neural network (DNN), which results in classification of fMRI brain scans more prone to ASD. The model can be trained to optimize the classifier while improving extracted features based on both reconstructed data error and the classifier error.

Abstract: A medical image processing apparatus includes: a display unit; and circuitry configured to: acquire volume data including tissues, and set a first mask region and a second mask region which include a voxel to be rendered among a plurality of voxels included in the volume data; set a first plane which intersects both the first mask region and the second mask region; display a first image in which a first region formed by cutting the first mask region by the first plane and the second mask region are rendered; receive through an operation unit a first operation for setting a second plane which is parallel to the first plane and intersects both the first mask region and the second mask region; and display a second image in which a second region formed by cutting the first mask region by the second plane and the second mask region are rendered.

Abstract: A method of facilitating processing of pathology images involves receiving pathology image data representing a pathology image having a plurality of image regions, wherein the pathology image data includes, for each of the plurality of image regions, a respective plurality of representations of the image region including a first representation and a second representation, the second representation having a smaller data size than the first representation. The method involves, for each of the plurality of image regions: determining, based at least in part on the first representation of the image region, a first set of image properties, determining whether the first set of image properties meets first image property criteria, and, if the first set of image properties meets the first image property criteria, producing signals for causing the second representation to be used in place of the first representation. Other methods, systems, and computer-readable media are disclosed.

Abstract: Provided are a system, method, and computer readable storage medium in which data is received from a dental imaging system. The received data is analyzed to adjust one or more imaging parameters of the dental imaging system.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for filtering a set of tracts that are predicted to be included in a selected neuro-network. In one aspect, a method comprises: receiving selection data selecting a network of a brain, processing magnetic resonance image data of the brain to identify a set of tracts that are predicted to be included in the selected network, determining a blocking surface for the selected network, comprising: obtaining a set of parcellations for the selected network and determining, based on a respective position in the brain of each parcellation in the set of parcellations, a set of parameters defining the blocking surface, and generating filtered tract data by filtering the set of tracts that are predicted to be included in the selected network to remove tracts that intersect the blocking surface.

Abstract: Systems and methods for medical imaging and analysis are described. The systems and methods comprise generating raw medical image data from a medical imaging hardware device, processing the raw medical image data, generating a processed raw medical image file, transmitting the processed medical image data file and imaging data, identifying a normalization factor based on the imaging detail data, normalizing the processed medical image data file using the normalization factor, and comparing the processed medical image data file with at least one other processed medical image data file. The difference between the processed medical image file and the at least one other processed medical image file is subtracted. A graphical representation of the difference is generated and displayed.

Abstract: A method for ascertaining pulmonary fibrosis disease progression or treatment response includes obtaining a first set of computed tomography (CT) images of a lung and determining a first Pulmonary Surface Index (PSI) score for the lung by detecting a first actual lung boundary of the lung within the first set of CT images, determining a first approximated lung boundary within the first set of CT images, and determining the PSI score using inputs based on the first actual lung boundary and the first approximated lung boundary. The method also includes obtaining a second set of CT images of the lung and determining a second PSI score for the lung using inputs based on a second actual lung boundary and a second approximated lung boundary. The method also includes assessing pulmonary fibrosis treatment response or disease progression based on the first PSI score and the second PSI score.

Abstract: The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

Abstract: An approach is provided for generating a super-resolution image as a higher resolution version of an input image. The approach, for example, involves determining a set of tasks to be performed on the input image to facilitate generating the super-resolution image. The approach also involves selecting a combination of loss functions, wherein each loss function of the combination of loss functions is respectively a task-specific neural network pre-trained to perform a corresponding one of the set of tasks. The approach also involves training the super resolution neural network using the combination of loss functions as one or more layers of the super resolution neural network. The approach also involves using the trained super resolution neural network to generate the super-resolution image as a higher resolution version of the input image.

Abstract: A method and an apparatus for extracting a lane line, a device, a computer readable-storage medium and a collection entity are provided. The method includes: obtaining a first group of lane lines of a road based on a first image generated from a point cloud collected by a laser radar; obtaining a second group of lane lines of the road based on a second image collected by a camera; and determining a lane line set of the road based on the first group of lane lines and the second group of lane lines.

Abstract: Techniques for processing multiplexed immunofluorescence (MxIF) images. The techniques include obtaining at least one MxIF image of a same tissue sample, obtaining information indicative of locations of cells in the at least one MxIF image, identifying multiple groups of cells in the at least one MxIF image at least in part by determining feature values for at least some of the cells using the at least one MxIF image and the information indicative of locations of the at least some cells in the at least one MxIF image and grouping the at least some of the cells into the multiple groups using the determined feature values, and determining at least one characteristic of the tissue sample using the multiple cell groups.

Abstract: An image processing apparatus is disclosed. The image processing apparatus includes a storage unit; a transceiver; and a processor for controlling the storage unit to store an input frame including a plurality of image areas having preset arrangement attributes and metadata including the preset arrangement attributes, control the transceiver to receive viewing angle information, and control the transceiver to transmit the metadata and image data of at least one image region corresponding to the viewing angle information among the plurality of image regions by using at least one of the plurality of transmission channels matched with the plurality of image regions.

Abstract: According to one embodiment, a medical information processing apparatus includes processing circuitry. The processing circuitry is configured to receive data acquired by scan for an object, and output a reconstructed image data based on the data and a trained model that accepts the data as input data and outputs the reconstructed image data corresponding to the data. The trained model is trained by learning using raw data generated based on a numerical phantom and the numerical phantom.

Abstract: Provided is a method for determining a position of an imaged anatomical body part of a patient. The method includes acquiring patient image data describing a digital image of at least part of a reference device and the anatomical body part, acquiring reference device model data describing a model of at least one of at least one internal surface or at least one external surface of the reference device, determining, —based on the patient image data and the reference device model data, reference device image position data describing a relative position between the reference device and the anatomical body part, acquiring reference device tracking data describing a position of the reference device in the tracking reference system, and determining, based on the reference device image position data and the reference device tracking data, body part tracking data describing a position of the anatomical body part in the tracking reference system.

Abstract: Techniques are provided for multi-modal sensitive recognition. A digital data set for an object is obtained according to a modality, where the digital data set includes digital representations of the object at different values of a dimension of relevance of the modality. A reference location associated with the object is identified. A modal descriptor is derived for the modality according to an implementation of a multi-modal recognition algorithm by deriving a set of feature descriptors for the reference location and at the different values of the corresponding dimension of relevance, calculating a set of differences between the feature descriptors in the set of feature descriptors, and aggregating the set of differences into the modal descriptor. A device is then configured to initiate an action as a function of the modal descriptor.

Abstract: An information processing apparatus includes a receiving unit and a controller. The receiving unit receives an extraction-area image indicating an extraction area. The extraction area includes a fill-in area in which a writer handwrites information. When an instruction to correct a recognition result for the information written in the fill-in area indicated by the extraction-area image is given, the controller causes a display unit to display a different extraction-area image similar to the extraction-area image.

Abstract: A system for facilitating lesion analysis accesses a first data structure comprising a plurality of entries including anatomic location information and annotation information associated with a plurality of lesions represented in a first set of cross-sectional images. The system displays a respective representation of each of the plurality of entries and presents a second set of cross-sectional images. The system receives user input triggering selection of a particular entry of the plurality of entries of the first data structure.

Abstract: A computer-implemented method, an apparatus, and a system for estimating synthetic values of quantitative metrics are provided. They involve calculating new, more accurate boundaries using a classifier based on local intensity and spatial estimators, for the segmentation mask provided by a non-local means patch-based segmentation in a test image, and estimating for the pixels of interest at least one synthetic value of a quantitative metric using a given value of the quantitative metric assigned to the reference images and the boundaries. The method, apparatus, and system provide the advantage of generating synthetic values directly comparable against known values for given subjects or against predetermined scales for diagnostic or prognostic purposes. In the specific case of Alzheimer's disease, the invention stretches the predictive range up to two full decades, which constitutes a significant advance in the field of medical diagnostics.

Abstract: An automatic detecting method and an automatic detecting apparatus using the same are provided. The automatic detecting apparatus includes an inputting unit, a dividing unit, a contouring unit, a range analyzing unit, a boundary analyzing unit, an edge detecting unit, an expanding unit and an overlapping unit. The dividing unit is used for dividing an overlooking image into four clusters via a clustering algorithm. The contouring unit is used for obtaining a contour. The range analyzing unit is used for obtaining a detecting range. The boundary analyzing unit is used for obtaining a circular boundary in the detecting range. The edge detecting unit is used for obtaining a plurality of edges in the circular boundary. The expanding unit is used for expanding the edges to obtain a plurality of expanded edges. The overlapping unit is used for overlapping the expanded edges and the contour to obtain a defect pattern.

Abstract: A method for processing multiple images according to various embodiments of the present invention may comprise the operations of: acquiring multiple images by using multiple cameras; extracting image information for each of the acquired multiple images; preprocessing the multiple images on the basis of the extracted image information, and generating parameters corresponding to the multiple images, respectively; selecting one or more images required according to a zoom magnification among the acquired multiple images; and applying parameters corresponding to the selected one or more images and performing image processing. Other embodiments are possible.