Abstract: A method for automated determination of a growth rate of an object in 3D data sets is described wherein the method may comprise: a first trained 3D detection deep neural network (DNN) determining one or more first VOIs in a current 3D data set and second VOIs in prior 3D data set, a VOI being associated with an abnormality; a registration algorithm, preferably a registration algorithm based on a trained 3D registration DNN, determining a mapping between the one or more first and second VOIs, the mapping providing for a first VOI in the current 3D data set a corresponding second VOI in the prior 3D data set; a second trained 3D segmentation DNN segmenting voxels of a first VOI into first voxels representing the abnormality and voxels of a corresponding second VOI into second voxels representing the abnormality; and, determining a first volume of the abnormality on the basis of the first voxels and a second volume of the abnormality on the basis of the second voxels and using the first and second volume to dete

Abstract: A computer-implemented method for determining a pathology in 3D image data is describe wherein the method may comprise:receiving at least a first 3D image of a body part, the 3D image comprising voxels associated with a predetermined image volume; a first 3D convolutional neural network determining a position of a volume of interest (VOI) in the image volume of the first 3D image, the VOI being associated with a pathology of the body part, the VOI defining a sub-volume of the image volume; determining first VOI voxels by selecting voxels of the first 3D image that have a position within the VOI as determined by the first 3D convolution neural network and providing the first VOI voxels to the input of a second 3D convolutional neural network; the second 3D convolutional neural network, determining a target label value on the basis of at least the first VOI voxels, the target label value being indicative of the presence or absence of a pathology in the VOI; and, generating a medical report by associating the ta

Abstract: A method for automated determination of a growth rate of an object in 3D data sets is described wherein the method may comprise: a first trained 3D detection deep neural network (DNN) determining one or more first VOIs in a current 3D data set and second VOIs in prior 3D data set, a VOI being associated with an abnormality; a registration algorithm, preferably a registration algorithm based on a trained 3D registration DNN, determining a mapping between the one or more first and second VOIs, the mapping providing for a first VOI in the current 3D data set a corresponding second VOI in the prior 3D data set; a second trained 3D segmentation DNN segmenting voxels of a first VOI into first voxels representing the abnormality and voxels of a corresponding second VOI into second voxels representing the abnormality; and, determining a first volume of the abnormality on the basisof the first voxels and a second volume of the abnormality on the basis of the second voxels and using the first and second volume to deter

Abstract: A computer-implemented method for determining a pathology in 3D image data is describe wherein the method may comprise:receiving at least a first 3D image of a body part, the 3D image comprising voxels associated with a predetermined image volume; a first 3D convolutional neural network determining a position of a volume of interest (VOI) in the image volume of the first 3D image, the VOI being associated with a pathology of the body part, the VOI defining a sub-volume of the image volume; determining first VOI voxels by selecting voxels of the first 3D image that have a position within the VOI as determined by the first 3D convolution neural network and providing the first VOI voxels to the input of a second 3D convolutional neural network; the second 3D convolutional ON neural network, determining a target label value on the basis of at least the first VOI voxels, the target label value being indicative of the presence or absence of a pathology in the VOI; and, generating a medical report by associating the