The present invention relates to a deep learning-based multiple skin lesion detection system, a multiple lesion detection method, and a computer-readable recording medium that has a program for implementing same recorded thereon. The system according to the present invention enables accurate classification and detection of various skin lesions having similar characteristics, on the basis of a context-dependent decision-making structure in which the local spatial correlation between various skin lesions in skin is considered.

An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n≥3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m≥2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.

An information processing unit includes: a diagnostic image input section that inputs the diagnostic image; an operation information obtaining section that obtains display operation history information representing an operation history of a user who controls displaying of the diagnostic image; a query image generation section that extracts a predetermined region of the input diagnostic image to generate a query image; a diagnosed image obtaining section that supplies the generated query image and the display operation history information to a diagnosed image search unit and obtains the diagnosed image obtained as a search result by the diagnosed image search unit; and a display control section that displays the diagnostic image and the obtained diagnosed image for comparison.

A short axis in a 3 dimensional image of a lesion is determined starting from voxels defining the long axis and voxels in the plane of the long axis. Voxels within the plane of the long axis are projected perpendicularly onto the long axis and receive an identifier indicative of the region on the long axis onto which they are projected. Distances between points (projected sub-voxels) in pairs of points within the same range and within adjacent ranges are evaluated in order to determine the longest distance.

The invention relates to a method for determining the spatial development of tumor tissue, by acquiring patient medical image data describing sequences of patient medical images of tumors in parts of patient bodies, wherein the patient medical images of each sequence have been taken at subsequent points in time and each sequence has been taken tier a different patient; determining, by additively fusing subsequent patient medical images of each sequence to one another, patient spatial development data describing the spatial development of a tumor in each patient body; acquiring atlas data describing an atlas representation of the parts of patient bodies; determining, based on the atlas data and the patient development data, development probability data describing a probability for a spatial development of a tumor.

There is provided a method for registration of intravital anatomical imaging modality image data and nuclear medicine image data of a patient's heart comprising: obtaining anatomical image data including a heart of a patient outputted by an anatomical intravital imaging modality; obtaining at least one nuclear medicine image data outputted by a nuclear medicine imaging modality, the nuclear medicine image data including the heart of the patient; identifying a segmentation of a network of vessels of the heart in the anatomical image data; identifying a contour of at least part of the heart in the nuclear medicine image data, the contour including at least one muscle wall border of the heart; correlating between the segmentation and the contour; registering the correlated segmentation and the correlated contour to form a registered image of the anatomical image data and the nuclear medicine image data; and providing the registered image for display.

The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

Kits, diagnostic systems and methods are provided, which measure the distribution of colors of skin features by comparison to calibrated colors which are co-imaged with the skin feature. The colors on the calibration template (calibrator) are selected to represent the expected range of feature colors under various illumination and capturing conditions. The calibrator may also comprise features with different forms and size for calibrating geometric parameters of the skin features in the captured images. Measurements may be enhanced by monitoring over time changes in the distribution of colors, by measuring two and three dimensional geometrical parameters of the skin feature and by associating the data with medical diagnostic parameters. Thus, simple means for skin diagnosis and monitoring are provided which simplify and improve current dermatologic diagnostic procedures.

Various methods and systems are provided for ultrasound imaging. In one embodiment, a method comprises acquiring, with an ultrasound transducer of a scanning apparatus during an ultrasound scan of a patient, an ultrasound image, detecting, with an artificial intelligence model, a region of interest within the ultrasound image including a possible tumor, acquiring, with the ultrasound transducer, an elastic image of tissue within the region of interest, and displaying, with a display device, the elastic image. In this way, shear wave elastography may be automatically targeted to a region of interest, thereby reducing the processing load for the analysis and enabling a higher elasticity imaging frame rate for three-dimensional ultrasound imaging.

System for performing fully automatic brain tumor and tumor-aware cortex reconstructions upon receiving multi-modal MRI data (T1, T1c, T2, T2-Flair). The system outputs imaging which delineates distinctions between tumors (including tumor edema, and tumor active core), from white matter and gray matter surfaces. In cases where existing MRI model data is insufficient then the model is trained on-the-fly for tumor segmentation and classification. A tumor-aware cortex segmentation that is adaptive to the presence of the tumor is performed using labels, from which the system reconstructs and visualizes both tumor and cortical surfaces for diagnostic and surgical guidance. The technology has been validated using a publicly-available challenge dataset.