PROCESSING MEDICAL IMAGES
FIG1 is a schematic illustration of an example of an apparatus for processing a medical image according to various embodiments
According to a first aspect, various embodiments disclosed herein provide an apparatus for processing a medical image associated with a subject. FIG1 is a simplified schematic illustration of an example of such an apparatus 100. The medical image may form part of a radiological examination (also referred to as a radiological examination study or a medical imaging study). In some embodiments, a radiological examination may comprise just a medical image while, in other embodiments, a radiological examination may comprise one or more medical images (e.g. a stack or series of medical images) in addition to other information, such as text, data included in one or more data fields, annotations or the like. The medical image may be acquired using any medical imaging modality or technique including, for example, x-ray imaging, magnetic resonance imaging (MRI), ultrasonography or ultrasound, positron emission tomography (PET), computed tomography (CT) and single-photon emission computed tomography (SPECT). Other imaging modalities that are familiar to those skilled in the art may also be used to acquire the medical image. The apparatus 100 further comprises a plurality of image processors 104, each image processor 104 for performing at least one image processing task in respect of the medical image. In the example shown in FIG1, the apparatus 100 includes the image processors 104a, 104b ... 104n-1, 104n, where n is the number of image processors. Each image processor 104 may, for example, comprise circuitry or an engine configured to perform one or more image processing tasks. In some examples, each image processor 104 may comprise at least one of: an artificial intelligence engine; an image processing engine; a predictive model engine; a machine learning engine; and a statistical analysis engine. A machine learning engine may employ machine learning techniques including, for example, decision tree algorithms, artificial neural networks, deep learning neural networks, support vector machines, Bayesian networks, and the like, which will be familiar to those skilled in the field of machine learning.
schematic
100
apparatus
106
102
clinical information extractor
104
processor processors
['1. An apparatus (100) for processing a medical image associated with a subject, the apparatus comprising: a clinical information extractor (102) for determining clinical information associated with the medical image; a plurality of image processors (104), each image processor for performing at least one image processing task in respect of the medical image; and an image processing manager (106) for determining, based at least on the determined clinical information associated with the medical image, at least one image processor of the plurality of image processors to perform one or more tasks in respect of the medical image.']