Embodiments of the disclosure provide systems and methods for generating a report based on a medical image of a patient. An exemplary system includes a communication interface configured to receive the medical image acquired by an image acquisition device. The system may further include at least one processor. The at least one processor is configured to automatically determine keywords from a natural language description of the medical image generated by applying a learning network to the medical image. The at least one processor is further configured to generate the report describing the medical image of the patient based on the keywords. The at least one processor is also configured to provide the report for display.

Embodiments described herein provide for training a machine learning model for automatic organ segmentation. A processor executes a machine learning model using an image to output at least one predicted organ label for a plurality of pixels of the image. Upon transmitting the at least one predicted organ label to a correction computing device, the processor receives one or more image fragments identifying corrections to the at least one predicted organ label. Upon transmitting the one or more image fragments and the image to a plurality of reviewer computing devices, the processor receives a plurality of inputs indicating whether the one or more image fragments are correct. When a number of inputs indicating an image fragment of the image fragments is correct exceeds a threshold, the processor aggregates the image fragment into a training data set. The processor trains the machine learning model with the training data set.

An image processing device includes a processor, and the processor is configured to output information for displaying a three-dimensional target part image as a three-dimensional image showing a target part, on a display device, calculate a virtual axis of the target part in the three-dimensional target part image, output information for displaying a two-dimensional image corresponding to a first cross section crossing a first position on the virtual axis, on the display device, change a geometrical characteristic of the first cross section in response to an instruction to change the geometrical characteristic, and output information for displaying a two-dimensional image corresponding to a second cross section obtained by changing the geometrical characteristic, on the display device.

A processor derives a first composition image representing a first composition included in a subject including three or more compositions from at least one radiation image acquired by imaging the subject, derives at least one removal radiation image obtained by removing the first composition from the at least one radiation image by using the first composition image, derives a plurality of other composition images representing a plurality of other compositions different from the first composition included in the subject by using the at least one removal radiation image, and derives a composite image obtained by synthesizing the first composition image and the plurality of other composition images at a predetermined ratio.

Supervised and unsupervised learning schemes may be used to automatically label medical images for use in deep learning applications. Large labeled datasets may be generated from a small initial training set using an iterative snowball sampling scheme. A machine learning powered automatic organ classifier for imaging datasets, such as CT datasets, with a deep convolutional neural network (CNN) followed by an organ dose calculation is also provided. This technique can be used for patient-specific organ dose estimation since the locations and sizes of organs for each patient can be calculated independently.

An image processing method includes: extracting a candidate modality image to be visualized from a data group of a single or a plurality of modality images; first associating separate modality data referring to an extracted modality image, with the extracted modality image; uniquely determining an image to be visualized, based on the modality image extracted at the extracting and an association result of the first associating; second associating different modality data being not associated at the first associating, with the modality image extracted at the extracting; and displaying the different modality data on the modality image extracted at the extracting.

An electronic endoscope system includes an image processing unit that uses a numerical value to evaluate an appearance feature appearing in a biological tissue by using an images captured by an electronic endoscope. The image processing unit calculates a first pixel evaluation value indicating a degree of a first feature, which is featured by a first color component or a first shape appearing in an attention area in the biological tissue, and which relates to the first color component or the first shape, for each pixel from the image, and calculates a first representative evaluation value relating to the first feature by integrating the first pixel evaluation value. Furthermore, the image processing unit evaluates a degree of a second feature that shares the first color component or the first shape with the first feature.

A system for automatically analyzing a video recording of a colonoscopy includes a processor and memory storing instructions, which when executed by the processor, cause the processor to receive the video recording of the colonoscopy performed on the colon and detect informative frames in the video recording. A frame is informative if the clarity of the frame is above a threshold or if the frame includes clinically relevant information about the colon. The instructions cause the processor to generate scores indicating severity levels of a disease for a plurality of the informative frames, estimate locations of the plurality of the informative frames in the colon, and generate an output indicating a distribution of the scores over one or more segments of the colon by combining the scores generated for the plurality of the informative frames and the estimated locations of the plurality of the informative frames in the colon.

A method for estimating parameters of an object which is to be estimated in a digital image which represents real imaged content, comprising at least: a) an initial step comprising the production of a dictionary of content components and the production of a dictionary of object components, the content components and the object components having the same dimensions as the digital image; b) a step of establishing, at the same time, the magnitude of each of the content components of the dictionary of content components and of the object components of the dictionary of object components present in the digital image; c) a step of establishing, from the magnitude of each of the object components, the value of at least one parameter which characterizes the object to be estimated.

An object recognition method is performed at an electronic device. The method includes: pre-processing a target image, to obtain a pre-processed image, the pre-processed image including three-dimensional image information of a target region of a to-be-detected object, processing the pre-processed image by using a target data model, to obtain a target probability, the target probability being used for representing a probability that an abnormality appears in a target object in the target region of the to-be-detected object; and determining a recognition result of the target region of the to-be-detected object according to the target probability, the recognition result being used for indicating the probability that the abnormality appears in the target region of the to-be-detected object. The object recognition method can effectively improve accuracy of object recognition and avoid a case of incorrect recognition.