Systems and methods for predicting outcome of radiation therapy is described herein. An example method includes receiving respective values for tumor volume of a target patients tumor at first and second time points, and calculating a change in tumor volume between the first and second time points. The method also includes estimating a patient-specific carrying capacity based on a logistic growth model and the change in tumor volume. Additionally, the method includes predicting a volume of the target patient's tumor at a future time point during radiation treatment based, at least in part, on a historical carrying capacity reduction fraction distribution and the patient-specific carrying capacity. The method further includes predicting a patient-specific outcome of radiation therapy for the target patient based, at least in part, on the predicted volume of the target patients tumor at the future time point.

A program and the like that make a catheter system relatively easy to use. The program including a non-transitory computer-readable medium (CRM) storing computer program code executed by a computer processor that executes a process comprising: acquiring a tomographic image generated using a diagnostic imaging catheter inserted into a lumen organ; and inputting the acquired tomographic image to a first model configured to output types of a plurality of objects included in the tomographic image and ranges of the respective objects in association with each other when the tomographic image is input, and outputting the types and ranges of the objects output from the first model.

A support device for an endoscope comprises a tubular member configured for removable attachment to an outer surface of the endoscope near, or at, its distal end and a plurality of projecting elements extending outward from the outer surface of the tubular member and circumferentially spaced from each other. The device includes an optically transparent cover coupled to the tubular member and configured for covering the distal end of the endoscope when the tubular member is attached to the outer surface of the endoscope. The projecting elements provide support for the endoscope, improve visualization and center the scope as it passes through a body lumen, such as the colon. In addition, the cover seals the distal end of the endoscope to protect the scope and its components from debris, fluid, pathogens and other biomatter.

Provided herein are techniques for label free cell sorting. The systems and methods provided herein may use machine learning based image classification techniques to identify cells of interest within a sample of cells. The cells of interest may then be separated from the sample using mechanical, pneumatic, piezoelectric, and/or electronic devices.

A margin assessment method is provided. Under cooperation of harmonic generation microscopy (HGM) and a deep learning method, the margin assessment method can instantaneously and digitally determine whether a 3D image group generated by an HGM imaging system is a malignant tumor or the surrounding normal skin, so as to assist in determining margins of a lesion.

An information processing apparatus (2000) detects an abnormal region (30) from a predetermined range (16) of a video frame (14). The information processing apparatus (2000) determines whether a predetermined condition is satisfied in a case where the abnormal region (30) is detected from the predetermined range (16) of a certain video frame (14) and the abnormal region (30) is not detected from the predetermined range (16) of a predetermined video frame (14) generated later than the video frame (14). In a case where the predetermined condition is not satisfied, the information processing apparatus (2000) performs a predetermined notification.

The present disclosure provides an image processing method, an image processing device, an electronic apparatus and a readable storage medium. The image processing method includes: obtaining feature map data of an input image; extracting a feature region in the feature map data in accordance with a size of a convolution kernel; performing windowing processing on the feature region; and obtaining a windowed feature map of the input image in accordance with the feature region obtained after the windowing processing.

Provided are a capsule endoscope apparatus for supporting a lesion diagnosis and a lesion diagnosis supporting method using the same. The capsule endoscope apparatus for supporting lesion diagnosis includes an imaging unit configured to capture one or more images of an inside of a body, a control unit configured to detect a suspected lesion region in the images and perform a precision diagnosis procedure when a suspected lesion region corresponding to a value equal to or greater than a certain threshold is detected, an image processing unit configured to process the images in the precision diagnosis procedure, and a communication module configured to transmit and receive processed images to another capsule endoscope apparatus or a terminal by using a wireless communication method.

A first image acquisition unit acquires a first image that is captured by a sensor having a first pixel arrangement pattern and includes a first reproduction band in a frequency domain. A second image acquisition unit acquires a second image that is captured by a sensor having a second pixel arrangement pattern and includes a second reproduction band different from the first reproduction band in the frequency domain. A correction processing unit generates a first correction image by correction processing of at least reducing or deleting high-frequency components that are not included in the second reproduction band within the first reproduction band.

The present invention relates to a method and system that automatically finds, segments and measures lesions in medical images following the Response Evaluation Criteria In Solid Tumours (RECIST) protocol. More particularly, the present invention produces an augmented version of an input computed tomography (CT) scan with an added image mask for the segmentations, 3D volumetric masks and models, measurements in 2D and 3D and statistical change analyses across scans taken at different time points. According to a first aspect, there is provided a method for determining volumetric properties of one or more lesions in medical images comprising the following steps: receiving image data; determining one or more locations of one or more lesions in the image data; creating an image segmentation (i.e. mask or contour) comprising the determined one or more locations of the one or more lesions in the image data and using the image segmentation to determine a volumetric property of the lesion.