Agfa Healthcare Nv

Vrvis Zentrum Fur Virtual Reality Und Visualisierung Forschungs-gmbh

Agfa Healthcare

Vrvis Zentrum Fur Virtual Reality Und Visualisierung

Agfa Nv

Imp Forschunginstitut Für Molekulare Pathologie Gmbh

Vrvis Zentrum Für Virtual Reality Und Visualisierung Forschungs-gmb

Vrvis Zentrum Füvirtual Reality Und Visualisierung Forschungs—gmbh

Katja Buehler

David Major

Maria Wimmer

Alexey Novikov

Dimitrios Lenis

Astrid Berg

Jiri Hladuvka

Eva Vandersmissen

Gert Sluiter

A method for finding image regions that significantly influence classification in a tool for pathology classification in a medical image wherein image regions that influence classification are inpainted, by replacing the pixels in these regions by representations of heathy tissue, resulting in an image with a healthy classification-score in the classification tool.

Method for finding image regions that significantly influence classification in a tool for pathology classification in a medical image

A method of augmenting the number of labeled images for training a neural network comprising the steps of—Starting from a dataset of labeled images with corresponding segmentation masks and a dataset of unlabeled images, gathering for a given image i in a data set of labeled images a number of images with similar metadata in said dataset of unlabeled images so as to form data sub-set Sim i,—Training a multiclass segmentation neural network on said labeled images thereby generating segmentation masks for the images in subset Sim i,—On the basis of these segmentation masks judging similarity between images of Sim i and image i and finding the most similar image(s) in Sim i by computing and comparing histograms of segmentation masks of image i and images in Sim i—Transferring the histogram of the most similar images in Sim i to given image i.

Method of augmenting the number of labeled images for training a neural network

Method of segmenting anatomical structures such as organs in 3D scans in an architecture that combines U-net, time-distributed convolutions and bidirectional convolutional LSTM.

Sequential segmentation of anatomical structures in 3D scans

A method for training an artificial neural network on an additional untrained segmentation task prevents the loss of previously acquired segmentation skills on originally trained segmentation tasks.

Avoiding catastrophic interference while training an artificial neural network on an additional task

A pipe-line method for multi-label segmentation of anatomic structures in a medical image using a convolutional neural network trained with a weighted loss function takes into account under&#x2014;representation of at least one anatomical structure in a ground-truth mask relative to other anatomical structures. Different architectures for the convolutional neural network are described.

Multi-class image segmentation method

A method and system for automatically detecting systemic arteries in arbitrary field-of-view computed tomography angiography (CTA) includes fully-automatically analyzing a medical image represented by a digital image representation.

Method for automatically detecting systemic arteries in arbitrary field-of-view computed tomography angiography (CTA)

A method, and an according apparatus and system, for analyzing medical images of blood vessels includes the steps of a) classifying a surrounding of at least one vessel represented in at least one medical image by applying a first classifier to the medical image such that the surrounding of the vessel is assigned to one of at least two surrounding classes, and b) segmentation of the at least one vessel dependent on the surrounding class to which the surrounding of the vessel has been assigned. The invention allows for a reliable segmentation and/or shape detection, in particular bifurcation detection, of blood vessels represented in medical images.

Method, apparatus and system for analyzing medical images of blood vessels

A method, apparatus, and system for reliably identifying a specific part of a spine in an image of a human or animal body, includes the steps of determining one or more parts of the spine in the image, determining one or more discriminative parameters for each of the one or more parts of the spine in the image, the discriminative parameters relating to at least one anatomical property of each of the one or more parts of the spine, classifying the discriminative parameters of the one or more parts of the spine in the image, and identifying a specific part of the spine based on the classification of the discriminative parameters of the one or more parts of the spine in the image. An identified vertebra, in particular the T12 vertebra and/or its associated intervertebral discs, can be used advantageously as a starting point of powerful automatic spine labeling algorithms.

Growing community of inventors

Mortsel, Belgium

Katja Buehler