A method for medical image data enhancement is provided. The method includes: receiving a medical image sample set related to an object to be detected; based on an attribute of the object lacking in the medical image sample set, selecting a first medical image and a second medical image from the medical image sample set, where the first medical image contains the object lacking the attribute, and the second medical image does not contain the object lacking the attribute; determining a first area image block containing the lacking attribute; determining a second area image block not containing the lacking attribute; generating a composite area image block by fusing the first area image block and the second area image block based on a mask including an object part and a peripheral part around the object part; embedding the composite area image block back into the second medical image to obtain a third medical image; including the third medical image in the medical image sample set to obtain a data-enhanced medical image sample set.

An image recording system includes a processor. The processor acquires a time series RAW image group including a plurality of time series RAW images in a first time section. The processor extracts, from the time series RAW image group, a recording candidate RAW image group included in a second time section as a part of the first time section. The processor records at least one RAW image included in the recording candidate RAW image group as a recording target RAW image which is a RAW image to be recorded. The processor selects the recording target RAW image from the recording candidate RAW image group. The processor converts the RAW image which is not selected as the recording target RAW image from the recording candidate RAW image group or the time series RAW image group to compressed data, and records the compressed data.

An image processing apparatus includes a processor including hardware, the processor being configured to: estimate, based on image information from a medical device that includes at least an endoscope, a plurality of procedure actions of an operator of the endoscope; perform different supports respectively for procedures by the operator, according to an estimation result of the procedure actions; and output a display for the supports to a display.

Various methods and systems are provided for tracking a biopsy target across one or more images. In one example, a method includes determining a position of a biopsy target in a selected image of a patient based on an image registration process with a reference image of the patient, and displaying a graphical representation of the position of the biopsy target on the selected image.

Provided is a capsule endoscope. The capsule endoscope includes: an imaging device configured to perform imaging on a digestive tract in vivo to generate an image; an artificial neural network configured to determine whether there is a lesion area in the image; and a transmitter configured to transmit the image based on a determination result of the artificial neural network.

The present disclosure describes a computer-implemented method for processing clinical three-dimensional image. The method includes training a fully supervised segmentation model using a labelled image dataset containing images for a disease at a predefined set of contrast phases or modalities, allow the segmentation model to segment images at the predefined set of contrast phases or modalities; finetuning the fully supervised segmentation model through co-heterogenous training and adversarial domain adaptation (ADA) using an unlabelled image dataset containing clinical multi-phase or multi-modality image data, to allow the segmentation model to segment images at contrast phases or modalities other than the predefined set of contrast phases or modalities; and further finetuning the fully supervised segmentation model using domain-specific pseudo labelling to identify pathological regions missed by the segmentation model.

Provided is a method of operating an apparatus for prostate cancer analysis operated by at least one processor, the method including: receiving digital slide images prepared from serial sections of a prostatectomy specimen, and a gross image of the serial sections; acquiring prostate cancer-related histological information of each received digital slide image using an artificial neural network model trained to infer histological information from the digital slide images; generating digital pathology images by displaying the prostate cancer-related histological information inferred from the artificial neural network model on each digital slide image; and providing a histological mapping image in which a tumor region extracted from each digital pathology image is mapped to a gross image of the corresponding section.

The present invention relates to an apparatus (10) for diagnostic image acquisition, comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to receive a data value relating to at least one biomarker in a measurement blood sample of a patient. The processing unit is configured to determine a time to acquire a diagnostic image of the patient, wherein the determination comprises utilization of the data value. The output unit is configured to output an indication of the time to acquire the diagnostic image of the patient.

An artificial intelligence-based image processing method implemented by a computer device is provided. The method includes: acquiring an image; performing element region detection on the image to determine an element region in the image; detecting a target element region in the image using an artificial intelligence-based technique; generating a target element envelope region by searching an envelope for the detected target element region; and fusing the element region and the target element envelope region to obtain a target element region outline.

Methods and systems for imaging and analysis are described. Accurate pressure ulcer measurement is critical in assessing the effectiveness of treatment. However, the traditional measuring process is subjective. Each health care provider may measure the same wound differently, especially related to the depth of the wound. Even the same health care provider may obtain inconsistent measurements when measuring the same wound at different times. Also, the measuring process requires frequent contact with the wound, which increases risk of contamination or infection and can be uncomfortable for the patient. The present application describes a new automatic pressure ulcer monitoring system (PrUMS), which uses a tablet connected to a 3D scanner, to provide an objective, consistent, non-contact measurement method. The present disclosure combines color segmentation on 2D images and 3D surface gradients to automatically segment the wound region for advanced wound measurements.