Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesellschaft

Florida State University Research Foundation, Inc.

Siemens Corporation

Siemens Healthcare Gmbh

Siemens Akteingesellschaft

Adrian Barbu

Dorin Comaniciu

Bogdan Georgescu

Wei Zhang

Luca Bogoni

Peter Durlak

Shaohua Kevin Zhou

Yefeng Zheng

Marcos Salganicoff

Martin Ostermeier

Zhuowen Tu

Simone Prummer

Le Lu

Vassilis Athitsos

Xiang Sean Zhou

David D Liu

Michael Scheuering

Michael Suehling

Ying Zhu

Stefan Böhm

Matthias Wolf

Sarang Lakare

Josue Anaya

Richard Socher

Norbert Strobel

Martin Huber

Sascha Seifert

Stefan Boehm

Adam K Galant

Jie Shao

Ulrich Bill

Alexander Cavallaro

Johannes Feulner

Jing Yang

Chandan K Reddy

Jonathan Dowdall

Michael Lynch

Xun (Jason) Xu

Imaging processing techniques using a trained convolution neural network (CNN) are described. In one or more implementations, an image processing system and method are provided for applying an image processing algorithm to a dataset of training images to generate a plurality of performance curves, constructing a loss function based upon the plurality of performance curves, training a convolutional neural network (CNN) to optimize the loss function to establish a trained convolutional neural network, predicting a specific tuning parameter for an image of interest using the trained convolution neural network and performing image processing of the image of interest using the specific tuning parameter and the image processing algorithm to generate a processed image of interest.

System and method for image processing using automatically estimated tuning parameters

A method and apparatus for hierarchical parsing and semantic navigation of a full or partial body computed tomography CT scan is disclosed. In particular, organs are segmented and anatomic landmarks are detected in a full or partial body CT volume. One or more predetermined slices of the CT volume are detected. A plurality of anatomic landmarks and organ centers are then detected in the CT volume using a discriminative anatomical network, each detected in a portion of the CT volume constrained by at least one of the detected slices. A plurality of organs, such as heart, liver, kidneys, spleen, bladder, and prostate, are detected in a sense of a bounding box and segmented in the CT volume, detection of each organ bounding box constrained by the detected organ centers and anatomic landmarks. Organ segmentation is via a database-guided segmentation method.

Method and system for hierarchical parsing and semantic navigation of full body computed tomography data

The present invention provides a system and method for generating a dataset of uncompressed color images taken with three digital cameras and exhibiting different levels of real noise, resulting from low-light conditions. For each scene there are, on average, two low-noise and two noisy images that are aligned at the pixel level both spatially and in intensity. In one embodiment, the dataset contains over 100 scenes and more than 400 images, including both 16-bit RAW formatted images and 8-bit BMP pixel and intensity aligned images from three digital cameras. The generated dataset of real noise images is useful in the evaluation of denoising algorithms.

System and method for generating a dataset for real noise reduction evaluation

A method and system for building a statistical four-chamber heart model from 3D volumes is disclosed. In order to generate the four-chamber heart model, each chamber is modeled using an open mesh, with holes at the valves. Based on the image data in one or more 3D volumes, meshes are generated and edited for the left ventricle (LV), left atrium (LA), right ventricle (RV), and right atrium (RA). Resampling to enforce point correspondence is performed during mesh editing. Important anatomic landmarks in the heart are explicitly represented in the four-chamber heart model of the present invention.

Method and system for generating a four-chamber heart model

An Active Random Field is presented, in which a Markov Random Field (MRF) or a Conditional Random Field (CRF) is trained together with a fast inference algorithm using pairs of input and desired output and a benchmark error measure.

Systems and methods for training an active random field for real-time image denoising

A method and system for detecting and tracking an ablation catheter tip in a fluoroscopic image sequence is disclosed. Catheter tip candidates are detected in each frame of the fluoroscopic image sequence using marginal space learning. The detected catheter tip candidates are then tracked over all the frames of the fluoroscopic image sequence in order to determine an ablation catheter tip location in each frame.

Method and system for catheter detection and tracking in a fluoroscopic image sequence

A method and system for automatically detecting and segmenting lymph nodes in a 3D medical image, such as a CT image, is disclosed. A plurality of lymph node center point candidates are detected in the 3D medical image. A lymph node candidate is segmented for each of the detected lymph node center point candidates. Lymph nodes are detected from the segmented lymph node candidates by verifying the segmented lymph node candidates using a trained lymph node classifier.

Method and system for automatic detection and segmentation of axillary lymph nodes

A virtual map of vessels of interest in medical procedures, such as coronary angioplasty is created so that doses of contrasting agent given to a patient may be reduced. A position of a coronary guidewire is determined and locations of vessel boundaries are found. When the contrast agent has dissipated, virtual maps of the vessels are created as new images. The locations of the determined vessel boundaries are imported to a mapping system and an image obtained without using a contrast agent is modified based on the imported locations of vessel boundaries. This creates a virtual map of the vessels.

Methods and apparatus for virtual coronary mapping

A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary.

Method and system for polyp segmentation for 3D computed tomography colonography

A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes.

User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography

A method and system for vessel segmentation in fluoroscopic images is disclosed. Hierarchical learning-based detection is used to perform the vessel segmentation. A boundary classifier is trained and used to detect boundary pixels of a vessel in a fluoroscopic image. A cross-segment classifier is trained and used to detect cross-segments connecting the boundary pixels. A quadrilateral classifier is trained and used to detect quadrilaterals connecting the cross segments. Dynamic programming is then used to combine the quadrilaterals to generate a tubular structure representing the vessel.

Method and system for vessel segmentation in fluoroscopic images

A method and system for extracting coronary vessels fluoroscopic image sequences using coronary digital subtraction angiography (DSA) are disclosed. A set of mask images of a coronary region is received, and a sequence of contrast images for the coronary region is received. For each contrast image, vessel regions are detected in the contrast image using learning-based vessel segment detection and a background region of the contrast image is determined based on the detected vessel regions. Background motion is estimated between one of the mask images and the background region of the contrast image, and the mask image is warped based on the estimated background motion to generate an estimated background layer. The estimated background layer is subtracted from the contrast image to extract a coronary vessel layer for the contrast image.

System and method for coronary digital subtraction angiography

A method and system for evaluating image segmentation is disclosed. In order to quantitatively evaluate an image segmentation technique, synthetic image data is generated and the synthetic image data is segmented to extract an object using the segmentation technique. This segmentation results in a foreground containing the extracted object and a background. The visibility of the extracted object is quantitatively measured based on the intensity distributions of the segmented foreground and background. The visibility is quantitatively measured by calculating the Jeffries-Matusita distance between the foreground and background intensity distributions. This method can be used to evaluate segmentation of vessels in fluoroscopic image sequences by coronary digital subtraction angiography (DSA).

Method and system for evaluating image segmentation based on visibility

A method and system for detecting 3D objects in images is disclosed. In particular, a method and system for Ileo-Cecal Valve detection in 3D computed tomography (CT) images using incremental parameter learning and ICV specific prior learning is disclosed. First, second, and third classifiers are sequentially trained to detect candidates for position, scale, and orientation parameters of a box that bounds an object in 3D image. In the training of each sequential classifier, new training samples are generated by scanning the object's configuration parameters in the current learning projected subspace (position, scale, orientation), based on detected candidates resulting from the previous training step. This allows simultaneous detection and registration of a 3D object with full 9 degrees of freedom. ICV specific prior learning can be used to detect candidate voxels for an orifice of the ICV and to detect initial ICV box candidates using a constrained orientation alignment at each candidate voxel.

Method and system for detection and registration of 3D objects using incremental parameter learning

A method for online optimization of guidewire visibility in fluoroscopic images includes providing an digitized image acquired from a fluoroscopic imaging system, the image comprising an array of intensities corresponding to a 2-dimensional grid of pixels, detecting a guidewire in the fluoroscopic image, enhancing the visibility of the guidewire in the fluoroscopic image, calculating a visibility measure of the guidewire in the fluoroscopic image, and readjusting acquisition parameters of the fluoroscopic imaging system wherein the guidewire visibility is improved.

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Tallahassee, FL, United States of America

Adrian Barbu