A system and method of computer-aided detection (CAD or CADe) of medical images that utilizes persistence between images of a sequence to identify regions of interest detected with low interference from artifacts to reduce false positives and improve probability of detection of true lesions, thereby providing improved performance over static CADe methods for automatic ROI lesion detection.

Described herein are systems, devices, and methods for aiding a user to scroll through or otherwise manipulate a stack of medical and tissue images. A system as described herein may comprise: a foot controller, configured to detect one or more of vertical and horizontal motion of a user’s foot to control image navigation, review, positioning, and viewing, functions; a computer; and a user interface.

An image processing device includes a processor and a memory that is provided in or connected to the processor. The processor executes a region selection process of selecting a portion of a plurality of tomographic images, which indicate a plurality of tomographic planes of an object, respectively, and have a first resolution, as a target region to be set to a second resolution higher than the first resolution, a resolution enhancement process of increasing the resolution of the target region to the second resolution to generate a high-resolution partial image, and a composite two-dimensional image generation process of generating a high-resolution composite two-dimensional image having the second resolution, using the high-resolution partial image.

An image processing device determines whether each tumor candidate regions detected from a plurality of tomographic images indicating a plurality of tomographic planes of an object is a tumor or a local mass of a mammary gland, selects a first tomographic image group from the plurality of tomographic images in a first region determined to be the tumor, selects a second tomographic image group from the plurality of tomographic images in a second region determined to be the local mass of the mammary gland, selects a third tomographic image group from the plurality of tomographic images in a third region other than the first region and the second region, and generates a composite two-dimensional image using the tomographic image groups selected for each of the first region, the second region, and the third region.

Examples of the present disclosure describe systems and methods for predicting the reading time and/or reading complexity of a breast image. In aspects, a first set of data relating to the reading time of breast images may be collected from one or more data sources, such as image acquisition workstations, image review workstations, and healthcare professional profile data. The first set of data may be used to train a predictive model to predict/estimate an expected reading time and/or an expected reading complexity for various breast images. Subsequently, a second set of data comprising at least one breast image may be provided as input to the trained predictive model. The trained predictive model may output an estimated reading time and/or reading complexity for the breast image. The output of the trained predictive model may be used to prioritize mammographic studies or optimize the utilization of available time for radiologists.

A method and breast imaging system for processing breast tissue image data includes feeding image data of breast images to an image processor, identifying image portions depicting breast tissue and high density elements and executing different processing methods on input images. A first image processing method involves breast tissue enhancement and high density element suppression, whereas the second image processing method involves enhancing high density elements. Respective three-dimensional sets of image slices may be generated by respective image processing methods, and respective two-dimensional synthesized images are generated and combined to form a two-dimensional composite synthesized image which is presented through a display of the breast imaging system. First and second image processing may be executed on generated three-dimensional image sets or two-dimensional projection images acquired by an image acquisition component at respective angles relative to the patient's breast.

A similar case retrieval apparatus includes: a lesion portion acquirer that acquires partial images including lesion portion images, an image feature extractor that extracts image features of each of the plurality of partial images; a location information acquirer that acquires location information of each of the partial images; a lateral position determiner that determines the right organ or the left organ in which each of the lesion portions exists based on the location information; a unilateral distribution identifier that determines whether or not a distribution of the lesion portions is a unilateral distribution; and a similar case retriever that retrieves case data from a case database including both case data for the unilateral distribution in the right organ and case data for the unilateral distribution in the left organ when the unilateral distribution identifier identifies that the distribution of the lesion portions is the unilateral distribution.

Apparatus for diagnosing breast cancer, the apparatus comprising a controller having a set of instructions executable to: acquire a contrast enhanced region of interest (CE-ROI) in an X-ray image of a patient's breast, the X-ray image comprising X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image; determine a texture neighborhood for each of a plurality of X-ray pixels in the CE-ROI, the texture neighborhood for a given X-ray pixel of the plurality of X-ray pixels extending to a bounding pixel radius of BPR pixels from the given pixel; generate a texture feature vector (TF) having components based on the indications of intensity provided by a plurality of X-ray pixels in the CE-ROI that are located within the texture neighborhood; and use a classifier to classify the texture feature vector TF to determine whether the CE-ROI is malignant.

A method for determining a view angle of a thermal image from a user and generating a suggestion to enable the user for adaptive positioning of a subject for capturing the thermal image is provided. The method includes (i) receiving a thermal image of a body of a subject, (ii) automatically determining a view angle of the thermal image from a user using a view angle estimator, (iii) determining an angular adjustment to be made to a view position of the thermal imaging camera or a position of the subject by comparing the determined view angle with a required view angle as per thermal imaging protocol when the thermal image does not meet the required view angle and (iv) generating instructions to the user for adjusting the view position of the thermal imaging camera for capturing a new thermal image at the required view angle as per thermal imaging protocol.

The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4/6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4/6 inhibitor therapy for HR+ metastatic breast cancer.