Abstract: For a given MR image having an anatomical landmark and a frame of reference, a method includes automatically selecting one or more series of MR images from a plurality of series of MR images previously acquired and having the anatomical landmark and the frame of reference. The method includes automatically selecting one or more candidate groups from the one or more series of MR images that were selected, wherein selection of the one or more candidate groups is based on predetermined rules. The method includes automatically selecting a respective reference image from each candidate group, wherein selection of each respective reference image is based on the predetermined rules. The method further includes automatically displaying the respective reference image selected for each candidate group, wherein each respective reference image is displayed in reference viewports on a user interface on a display based on the predetermined rules.

Abstract: A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.

Abstract: Methods and systems for addressing malfunction of a medical imaging device are disclosed. The method includes classifying a type of an image artifact in a medical image acquired by the medical imaging device by using a trained machine learning model. The method also includes analyzing system data associated with acquisition of the medical image to identify one or more system parameters that might have contributed to the type of image artifact and providing an action for addressing the image artifact based on the identified one or more system parameters.

Abstract: Methods and systems for addressing malfunction of a medical imaging device are disclosed. The method includes classifying a type of an image artifact in a medical image acquired by the medical imaging device by using a trained machine learning model. The method also includes analyzing system data associated with acquisition of the medical image to identify one or more system parameters that might have contributed to the type of image artifact and providing an action for addressing the image artifact based on the identified one or more system parameters.

Abstract: A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.