Abstract: Analysis of magnetic resonance (MR) images for identifying objects such as organs and radioactive seeds is improved through image pre-processing and post-processing techniques. With pre-processing, MR image data is analyzed to identify a set of biased image units. The image unit may be a voxel or a pixel. These biased image units are removed and replaced with image units having a random distribution centered around a specified value. The replacement image units approximate noise and reduce bias that may affect the image analysis. The post-processing techniques involve handling the results of multiple machine learning models. Each image unit is evaluated by multiple machine learning models. The probability of the image unit as corresponding to a particular object based on all machine learning models is determined. The particular object may be displayed based on the determined probability.

Abstract: Disclosed herein are systems and methods for determining quality metrics of images based on an edge gradient profile and characterizing regions of interest in an image or images. According to an aspect, a method includes using an imaging device to acquire one or more images including at least a portion of an organ of a subject. The method also includes computing an edge profile across an organ interface of a subject. The method also includes computing an edge gradient profile from the edge profile of a subject. The method also includes computing a image quality metric related to the spatial resolution of the image or images from the edge gradient profile. The method also includes defining multiple regions of interest within the portion of the organ. Further, the method includes characterizing the regions of interest based on predetermined criteria. The method also includes presenting the characterization of the edge gradient profile and characterization of the regions of interest to a user.

Abstract: Disclosed herein are systems and methods for determining quality metrics of images based on an edge gradient profile and characterizing regions of interest in an image or images. According to an aspect, a method includes using an imaging device to acquire one or more images including at least a portion of an organ of a subject. The method also includes computing an edge profile across an organ interface of a subject. The method also includes computing an edge gradient profile from the edge profile of a subject. The method also includes computing a image quality metric related to the spatial resolution of the image or images from the edge gradient profile. The method also includes defining multiple regions of interest within the portion of the organ. Further, the method includes characterizing the regions of interest based on predetermined criteria. The method also includes presenting the characterization of the edge gradient profile and characterization of the regions of interest to a user.

Abstract: Disclosed herein are systems and methods for determining quality metrics of images based on an edge gradient profile and characterizing regions of interest in an image or images. According to an aspect, a method includes using an imaging device to acquire one or more images including at least a portion of an organ of a subject. The method also includes computing an edge profile across an organ interface of a subject. The method also includes computing an edge gradient profile from the edge profile of a subject. The method also includes computing a image quality metric related to the spatial resolution of the image or images from the edge gradient profile. The method also includes defining multiple regions of interest within the portion of the organ. Further, the method includes characterizing the regions of interest based on predetermined criteria. The method also includes presenting the characterization of the edge gradient profile and characterization of the regions of interest to a user.