A framework for quality-driven image processing. In accordance with one aspect, image data and anatomical data of a region of interest are received. Zonal information is generated based on the anatomical data. Image processing is performed based on the image data to generate an intermediate image. One or more image quality metrics may then be determined for the intermediate image data using the zonal information. A processing action may be performed based on the one or more image quality metrics to generate a final image.

Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.

A method of generating a color image using a monochromatic image sensor. The method includes sequentially illuminating a surface in a plurality of colors, one color at a time. The monochromatic image sensor captures a plurality of image frames of the surface based on the plurality of colors. The plurality of image frames are identified, and at least one feature in the target of the plurality of image frames is highlighted. Color intensities of the plurality of image frames are normalized. A color intensity map of the target for each of the plurality of image frames is generated. A correlation score is determined by comparing each color intensity map of the plurality of image frames. The color image is generated based on the correlation score.

Provided are a method and system for automatically managing clinical trial image evaluation data. The method automatically managing clinical trial image evaluation data comprises the operations of: obtaining first image evaluation data and second image evaluation data from a first evaluator and a second evaluator, respectively; analyzing the obtained image evaluation data by evaluation items, whether the obtained image evaluation data conforms to preset evaluation rules; in a case in which the obtained image evaluation data includes an evaluation item which does not conform to the evaluation rules, automatically providing a query including a correction request and help with respect to the evaluation item that does not conform to the evaluation rules; and determining whether the first image evaluation data and the second image evaluation data match each other with respect to the evaluation items.

The disclosure relates to a system for analysis of medical image data, which represents a two-dimensional or three-dimensional medical image. The system is configured to read and/or determine, for the medical image, a plurality of image quality metrics and to determine a combined quality metrics based on the image quality metrics. The system is further configured so that the determination of the combined quality metrics takes into account an interaction between the image quality metrics in their combined effect on the combined quality metrics.

A system provides assistance during bottle-feeding. Video images of a subject bottle-feeding an infant are captured and displayed. Using image analysis, a reorientation of the bottle and/or infant is determined that is required in order to reach a desired bottle orientation and/or infant orientation. Reorientation instructions are provided in combination with the video images to assist the subject in reorienting the bottle and/or the infant to achieve the desired bottle orientation.

Using a computer-implemented intermediary by which contouring performed by two participants, such as two physicians, can be compared. First, contouring performed by each participant can be compared to contouring performed by the intermediary. Then, by way of the common intermediary and a transitive analysis, contouring performed by each participant can be compared.

There is provided an information processing apparatus including a processor and a memory connected to or built into the processor. The processor is configured to process a captured image by using an AI method that uses a neural network and perform composition processing of combining a first image obtained by processing the captured image by using the AI method and a second image obtained by processing the captured image without using the AI method.

An image segmentation method includes the following steps: obtaining a target image; inputting the target image into a machine learning model to obtain an image segmentation parameter value corresponding to the target image; executing an image segmentation algorithm on the target image according to the image segmentation parameter value to obtain an image segmentation result, wherein the image segmentation result is segmenting the target image into object regions; and displaying the image segmentation result. In addition, an electronic device and storage medium using the method are also provided.

Systems, methods, and instrumentalities are disclosed for computer vision-based surgical workflow recognition using natural language processing (NLP) techniques. Surgical video of surgical procedures may be processed and analyzed, for example, to achieve workflow recognition. Surgical phases may be determined based on the surgical video and segmented to generate an annotated video representation. The annotated video representation of the surgical video may provide information associated with the surgical procedure. For example, the annotated video representation may provide information on surgical phases, surgical events, surgical tool usage, and/or the like.