Ultrasound image segmentation is strongly influenced by the quality of data. There are characteristic artefacts which make the segmentation task complicated such as attenuation, speckle, shadows and signal dropout; due to the orientation dependence of acquisition that can result in missing boundaries. Further complications arise as the contrast between areas of interest is often low. However, there have been recent advances in transducer design, spatial/temporal resolution, digital systems, portability etc that mean that the quality of information from an ultrasound (US) device has significantly improved.
Allows to perform registration and segmentation for image analysis. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. ITK uses a model of software development known as Extreme Programming. The sampled representation is an image acquired from such medical instrumentation as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) or ultrasound scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both.
Offers a framework dedicated to image segmentation. SegAN proposes an approach derived from Generative Adversarial Networks (GANs) framework, trained on whole images with the aim of optimizing segmentation tasks in medical images. The application is based on multi-scale loss function for both the segmentor and critic networks. The application was tested on the segmentation of brain tumor images.
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