Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.

Abstract: A method and apparatus for medical image synthesis across image modalities or domains is disclosed, which synthesizes a target medical image based on a source medical image. A plurality of image patches are cropped from the source medical image. A synthesized target medical image is then generated from the source medical image by jointly performing sparse coding between each image patch of the source medical image and a corresponding image patch of the synthesized target image based on jointly trained source and target dictionaries.

Abstract: A connection is provided between first and second building structural members, wherein a connector is made up of a stiffener and a connection element, and the stiffener nests within the connection element, and both the stiffener and the connection element are attached to the second building structural member with the same fastener or fasteners, and similarly, both the stiffener and the connection element are attached to the first building structural member with the same fastener or fasteners.

Abstract: A method and apparatus for unsupervised cross-modal medical image synthesis is disclosed, which synthesizes a target modality medical image based on a source modality medical image without the need for paired source and target modality training data. A source modality medical image is received. Multiple candidate target modality intensity values are generated for each of a plurality of voxels of a target modality medical image based on corresponding voxels in the source modality medical image. A synthesized target modality medical image is generated by selecting, jointly for all of the plurality of voxels in the target modality medical image, intensity values from the multiple candidate target modality intensity values generated for each of the plurality of voxels. The synthesized target modality medical image can be refined using coupled sparse representation.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A radio-frequency identification (RFID)-based system for automatically tracking objects used in surgical procedures, and methods of use thereof, are disclosed. More particularly, an objects monitoring system is provided that includes a set of RFID tagged-objects (e.g., RFID tagged-surgical instruments), an RFID reader, an RFID reader antenna installed in a disposable wearable article, a computing device, and optionally a centralized server.

Abstract: A method and system for approximating a deep neural network for anatomical object detection is discloses. A deep neural network is trained to detect an anatomical object in medical images. An approximation of the trained deep neural network is calculated that reduces the computational complexity of the trained deep neural network. The anatomical object is detected in an input medical image of a patient using the approximation of the trained deep neural network.

Abstract: A method and apparatus for medical image synthesis across image modalities or domains is disclosed, which synthesizes a target medical image based on a source medical image. A plurality of image patches are cropped from the source medical image. A synthesized target medical image is then generated from the source medical image by jointly performing sparse coding between each image patch of the source medical image and a corresponding image patch of the synthesized target image based on jointly trained source and target dictionaries.

Abstract: A method and apparatus for cross-domain medical image synthesis is disclosed. A source domain medical image is received. A synthesized target domain medical image is generated using a trained contextual deep network (CtDN) to predict intensities of voxels of the target domain medical image based on intensities and contextual information of voxels in the source domain medical image. The contextual deep network is a multi-layer network in which hidden nodes of at least one layer of the contextual deep network are modeled as products of intensity responses and contextual response.

Abstract: A sealable settings dial assembly includes a casing for electrical equipment, such as an overload relay, and a rotatable dial. The casing includes a wire slot through which to extend a wire of a wire lock mechanism. The rotatable dial is movable relative to the casing, and is used to select a setting from a plurality of different settings for the electrical equipment. The dial includes spaced apart setting channels, such as setting holes or grooves, associated with the different settings to be selected. On the dial, each setting has an associated setting channel which aligns with the wire slot of the casing when the particular setting is selected on the dial. In that way, the wire can be extended through the wire slot and the setting channel for the selected setting to lock the dial at the selected setting.

Abstract: A method and apparatus for unsupervised cross-modal medical image synthesis is disclosed, which synthesizes a target modality medical image based on a source modality medical image without the need for paired source and target modality training data. A source modality medical image is received. Multiple candidate target modality intensity values are generated for each of a plurality of voxels of a target modality medical image based on corresponding voxels in the source modality medical image. A synthesized target modality medical image is generated by selecting, jointly for all of the plurality of voxels in the target modality medical image, intensity values from the multiple candidate target modality intensity values generated for each of the plurality of voxels. The synthesized target modality medical image can be refined using coupled sparse representation.

Abstract: An apparatus for slicing fruits, vegetables and other food items into slices of varying thickness is designed to work in combination with the user's own kitchen knives rather than having a preinstalled fixed blade of limited operable life span. The apparatus includes means for properly orienting and securing knife blades of a variety of shapes and sizes within the apparatus as well as safety features to prevent accidental injury.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: Object detection uses a deep or multiple layer network to learn features for detecting the object in the image. Multiple features from different layers are aggregated to train a classifier for the object. In addition or as an alternative to feature aggregation from different layers, an initial layer may have separate learnt nodes for different regions of the image to reduce the number of free parameters. The object detection is learned or a learned object detector is applied.

Abstract: A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective deep neural network is trained for each of the marginal search spaces, resulting in a series of trained deep neural networks. Each of the trained deep neural networks can evaluate hypotheses in a current parameter space using discriminative classification or a regression function. An anatomical object is detected in a medical image by sequentially applying the series of trained deep neural networks to the medical image.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A tissue retractor device is provided. The tissue retractor device includes a handle attached to an inflatable rake-shaped tissue retractor head.