Abstract: A non-transitory computer-readable storage medium storing a set of instructions executable by a processor. The set of instructions is operable to receive a current patient medical image of a current patient, compare the current patient medical image to a plurality of previous patient medical images, each of the previous patient medical images corresponding to a previous patient, select one of the previous patients based on a geometric similarity between the previous patient medical image of the selected one of the previous patients and the current patient medical image, and determine an initial radiation treatment plan based on a radiation treatment plan of the selected one of the previous patients.

Abstract: A non-transitory computer-readable storage medium storing a set of instructions executable by a processor. The set of instructions is operable to receive a current patient set of data relating to a current patient; compare the current patient set of data to a plurality of previous patient sets of data, each of the previous patient sets of data corresponding to a previous patient; select one of the previous patient sets of data based on a level of similarity between the selected previous patient set of data and the current patient set of data; and provide the selected previous patient set of data to a user.

Abstract: Provided herein is a system and method for performing a magnetic resonance imaging scan using a MR scanner. The method can comprise receiving via a user interface a MR imaging protocol categorizable into a MR scan type of a predefined set of MR scan types. Further, the method can comprise querying a database by providing to the database scan information permitting the database to identify the MR scan type of the MR imaging protocol. The method can further comprise receiving from the database statistical information on the MR scan type which can include statistics on modifications of individual scan parameters of the MR scan type, and providing the statistical information to the user interface. Modifications of the MR imaging protocol can be received from the user interface, resulting in a modified MR imaging protocol, according to which the MR imaging scan can be performed.

Abstract: A system for suggesting autocompletion terms during text entry of a report. A text entry unit (2) for enabling a user to enter a text into a current report (1). A term selector (11) for selecting at least one frequently co-occurring term (12), based on an extracted term (8), the extracted term section (9), the current section (10), and a co-occurrence statistic (7). An indicator (13) provides an indication of the at least one frequently co-occurring term (12) to the user. A system for analyzing reports comprises a co-occurrence statistics generator (26) for generating a plurality of co-occurrence statistics, a co-occurrence statistic being indicative of a first term, a first section, a second term, a second section, and a frequency in which reports contain the first term in the first section in combination with the second term in the second section.

Abstract: A system for aiding report authoring is disclosed. The system comprises a set of associations (1), wherein an association links at least one first concept term in a knowledge domain with at least one second concept term in the knowledge domain, wherein the association indicates a frequent co-occurrence of the at least one first concept term and the at least one second concept term in reports in the knowledge domain. The system comprises a concept term extractor (2) for extracting at least one first concept term (6) relating to a particular subject from a data record (7) associated with the particular subject, the data record (7) comprising an at least partially completed report (5) relating to the particular subject.

Abstract: A clinical decision support (CDS) system comprises: a case grouping sub-system (10) including a graphical user interface (30) that is operative to simultaneously display data representing a plurality of patient cases and further configured to enable a user to group selected patient cases represented by the simultaneously displayed data into clinically related groups (32) as selected by the user; a probative features determination sub-system (12) that is operative to determine probative features (44) that correlate with the clinically related groups; and a CDS user interface (16) that is operative to receive current patient data relating to a current patient case and to output clinical decision support information based on values of the probative features determined from the received current patient data.

Abstract: A classifier (20) is trained by a feature matrix (18, 18?) made up of feature vectors (F11, . . . , Fkm). The feature vectors are generated by operating on each of a plurality (k) of training image data sets with each of a plurality (m) of image processing algorithms (121, . . . , 12m) to generate processed and segmented images. Features of the segmented regions are extracted (14) to generate the feature vectors. In this manner, the classifier is trained with data generated with a variety of image processing algorithms.

Abstract: A system and method of providing computer-aided analysis of medical images uses an image processor (910) to process medical image data. A decision engine (920) generates a diagnosis based on the image data (940). The decision engine estimates the probability of an illness based on the image data and assesses the relevance of any unavailable data. The result is used to request this unavailable data from the user for computing a more complete diagnosis or otherwise displaying the results in incomplete form due to either the lack of additional data or the confidence in the incomplete diagnostic results. The diagnostic results may be displayed on an output terminal (970) and may be stored in the database (930).

Abstract: Methods are herein provided for decision support in diagnosis of a disease in a subject, and for extracting features from a multi-slice data set. Systems for computer-aided diagnosis are provided. The systems take as input a plurality of medical data and produces as output a diagnosis based upon this data. The inputs may consist of a combination of image data and clinical data. Diagnosis is performed through feature selection and the use of one or more classifier algorithms.

Abstract: The invention relates to search for cases in a database. According to the proposed method and apparatus, similarity matching is performed between an input case and a set of cases in an initial search to receive similar cases by—using a given matching criterion. Then statistics on image and non-image-based features associated with the similar cases are calculated and presented to the user with the similar cases. In a search refinement the similar cases are refined by additional features that are determined by the user based on the statistics. The search refinement can be iterative depending on the user's need.

Abstract: This invention relates to a method and device for case-based decision support. It proposes that a case-based decision support system is trained on inputs from several radiologists in order to have a “baseline” system, and then the system provides an option to a radiologist to refine the baseline system based on his/her inputs which either refine weights of features for similarity distance computation directly or provide new similarity ground truth clusters. By enabling modifying the similarity distance computation based on user inputs, this invention adapts similarity ground truth to different users with different experience and/or different opinions.