The present invention provides a comprehensive detection device and method for a cancerous region, and belongs to the technical field of deep learning. In the present invention, a cancerous region detection network is trained for preprocessed and annotated CT image data to predict bounding box coordinates of a cancerous region and a corresponding cancer confidence score; a clinical analysis network is trained for preprocessed clinical data with a cancer risk level to predict a cancer probability value of a corresponding patient; and a predicted cancer probability value is weighted to a predicted cancer confidence score to realize a comprehensive determination of the cancerous region. The present invention can detect a cancerous region with high accuracy and high performance.

A myelin image is generated with a stable contrast regardless of an imaging condition with an MRI apparatus that measures an echo signal generated from a subject by applying a high-frequency magnetic field and a gradient magnetic field to the subject placed in a static magnetic field according to a predetermined imaging sequence. A reconstructed image is obtained from the echo signal. A distribution of a quantitative value of the subject is estimated using a plurality of the reconstructed images, each of which is obtained by a plurality of types of imaging having different imaging conditions of the imaging sequence, and a signal function defining a relationship between the quantitative value of the subject and a signal value of the reconstructed image. An image generation unit generates an arbitrary image from the distribution of the quantitative value.

Provided is a technology for extracting an image of a target plane from 2D or 3D image data acquired by a medical imaging apparatus with a small amount of computation and at high speed. A plane of a target plane including a predetermined structure is extracted from image data of a subject. A region of the predetermined structure included in the plane is detected by applying a learning model learned using learning data including a target plane for learning including an image of the structure and a region-of-interest plane for learning obtained by cutting out and enlarging a partial region including the structure in the target plane for learning to a plurality of planes obtained from the image data, and the plane of the target plane is extracted based on the detected region of the predetermined structure.

The present disclosure provides methods and systems for image segmentation. The methods may include obtaining an initial image. The methods may include determining, based on the initial image, at least one target image using a positioning model. Each of the at least one target image may include a portion of a target region. The methods may further include, for the each of the at least one target image, determining an initial segmentation result of the portion of the target region using a segmentation model.

A dynamic image analysis system includes a modality and a hardware processor. The modality generates first and second dynamic images each composed of frame images indicating dynamics of a subject. The processor detects a first signal value of a pixel in a first region in each of the frame images of the first dynamic image. The hardware processor detects a second signal value of a pixel in a second region in which an identical site to that of the first region is imaged, the second region being in each of the frame images of the second dynamic image. The processor determines an image processing condition for making the first signal value close to the second signal value based on the first and second signal values detected. The hardware processor performs image processing on the first dynamic image under the determined image processing condition to generate a processed dynamic image.

The present disclosure provides systems and methods to receiving OCT or IVUS image data frames to output one or more representations of a blood vessel segment. The image data frames may be stretched and/or aligned using various windows or bins or alignment features. Arterial features, such as the calcium burden, may be detected in each of the image data frames. The arterial features may be scored. The score may be a stent under-expansion risk. The representation may include an indication of the arterial features and their respective score. The indication may be a color coded indication.

The medical image processing apparatus according to the present embodiment includes processing circuitry. The processing circuitry is configured to acquire contrast image data generated by imaging a subject. The processing circuitry is configured to input the acquired contrast image data to a learned model to generate a time phase data classified according to a contrast state of a lesion area with a contrast agent included in the acquired contrast image data, the learned model being for generating the time phase data based on the acquired contrast image data.

There are provided a proposal system, a proposal method, and a program that evaluate chronological information about a user to make a proposal related to hair care. The proposal system 1 includes a proposal unit 211 that acquires chronological information about the user over a predetermined period of time to generate the proposal based on the chronological information.

A medical system for assisting in the planning and/or performance of the reconstruction of normally symmetrical body parts of a patient comprises a measuring unit for measuring a first surface and a second surface for determining a topography of the first and second surfaces, a storage unit for storing the determined topographies of the first and second surfaces, an evaluation unit for establishing a mirror image of the stored topography of the first surface and for calculating deviations of the stored topography of the second surface from the first surface mirror image, and a control unit for outputting guidance information for at least one medical instrument on the basis of the calculated deviations of the topography of the second surface, in order to reconstruct the topography of the second surface according to the mirror image of the topography of the first surface.

Methods and systems for characterizing fluids from a patient are disclosed. The method includes receiving a time series of images of a conduit receiving fluids from the patient, identifying a conduit image region in each of the images, classifying a flow type through the conduit based on an evaluation of the conduit image region in the time series of images, and estimating at least one of a volume of fluids and a quantity of a blood component that has passed through the conduit within a predetermined period of time, based at least in part on the classification of the flow type.