The present disclosure is directed to a method and system for automatically predicting a physiological parameter based on images of vessel. The method includes receiving the images of a vessel acquired by an imaging device. The method further includes determining a sequence of temporal features at a sequence of positions on a centerline of the vessel based on the images of the vessel, and determining a sequence of structure-related features at the sequence of positions on the centerline of the vessel. The method also includes fusing the sequence of structure-related features and the sequence of temporal features at the sequence of positions respectively. The method additionally includes determining the physiological parameter for the vessel at the sequence of positions, by using a sequence-to-sequence neural network configured to capture sequential dependencies among the sequence of fused features.

For decision support in a medical therapy, machine learning provides a machine-learned generator for generating a prediction of outcome for therapy personalized to a patient. The outcome prediction may be used to determine dose. To assist in decision support, a regression analysis of the cohort used for machine training relates the outcome from the machine-learned generator to the dose and an actual control time (e.g., time-to-event). The dose that minimizes side effects while minimizing risk of failure to a time for any given patient is determined from the outcome for that patient and a calibration from the regression analysis.

A computer-implemented method for evaluating an image data set of an imaged region comprises: determining, from the image data set, at least two processed data sets having different image data content; applying a first sub-algorithm, of an evaluation algorithm, to a first of at least two processed data sets to determine a first intermediate result relating to image data content of the first of the at least two processed data sets; applying a second sub-algorithm, of the evaluation algorithm, to a second of the at least two processed data sets to determine a second intermediate result relating to image data content of the second of the at least two processed data sets; determining quantitative evaluation result data by a third sub-algorithm of the evaluation algorithm, wherein the third sub-algorithm uses both the first intermediate result and the second intermediate result as input data.

According to one embodiment, a medical information processing apparatus includes processing circuitry which updates a model for calculating an effect evaluation value for a medical decision. The processing circuitry updates a parameter of the model while retaining the structure of the model so that the structure of the model is updated less frequently than the parameter.

An image processing device includes at least one processor. The processor detects a specific structural pattern indicating a lesion candidate structure for a breast in a series of a plurality of projection images obtained by performing tomosynthesis imaging on the breast or in a plurality of tomographic images obtained from the plurality of projection images, synthesizes the plurality of tomographic images to generate a synthesized two-dimensional image, specifies a priority target region, in which the specific structural pattern is present, in the synthesized two-dimensional image, and performs determination regarding a diagnosis of a lesion on the basis of the synthesized two-dimensional image and the priority target region.

Embodiments of the disclosure provide methods and systems for multi-modality joint analysis of a plurality of vascular images. The exemplary system may include a communication interface configured to receive the plurality of vascular images acquired using a plurality of imaging modalities. The system may further include at least one processor, configured to extract a plurality of vessel models for a vessel of interest from the plurality of vascular images. The plurality of vessel models are associated with the plurality of imaging modalities, respectively. The at least one processor is also configured to fuse the plurality of vessel models associated with the plurality of imaging modalities to generate a fused model for the vessel of interest. The at least one processor is further configured to provide a diagnostic analysis result based on the fused model of the vessel of interest.

A method and system for creating a dynamic self-learning medical image network system, wherein the method includes receiving, from a first node initial user interaction data pertaining to one or more user interactions with the one or more initially obtained medical images; training a deep learning algorithm based at least in part on the initial user interaction data received from the node; and transmitting an instance of the trained deep learning algorithm to the first node and/or to one or more additional nodes, wherein at each respective node to which the instance of the trained deep learning algorithm is transmitted, the trained deep learning algorithm is applied to respective one or more subsequently obtained medical images in order to obtain a result.

Methods and systems are provided for detecting focal lesions in multi-phase or multi-sequence medical imaging studies of medical images. One system includes memory for storing medical images and an electronic processor. The electronic processor is configured to: detect native candidate lesions for first phase computer tomography (CT) scans, detect candidate lesions for second phase CT scans, and project the candidate lesions detected for the first phase CT scans and/or the second phase CT scans on a same common domain. Once the candidate lesions are registered together, the electronic processor performs a matching algorithm for each of the native candidate lesions of the first phase CT scans with the registered candidate lesions from the second phase CT scans to determine presence and contours of valid lesions for the medical images, and to discard candidate lesions that are not acceptable.

A medical image processing apparatus according to an embodiment includes processing circuitry. The processing circuitry acquires medical images of multiple time phases. The processing circuitry generates a vascular territory image showing plural vascular territories included in the subject tissue. The processing circuitry sets a region of interest in the subject tissue. The processing circuitry sets at least two regions out of the vascular territories and an ischemia area in the region of interest based on the vascular territory image. The processing circuitry calculates a ratio of each of the at least two regions to the region of interest. The processing circuitry outputs information about the ratio.

A fat mass derivation device includes at least one processor, in which the processor derives a fat mass distribution of a subject from a first radiation image and a second radiation image acquired by imaging the subject with radiation having different energy distributions, and derives a visceral fat mass distribution of the subject based on a shape of the fat mass distribution in a cross section orthogonal to a body axis of the subject.