Abstract: A video is segmented into a plurality of sequences corresponding to different facial states performed by a patient in the video. For each sequence, displacement of a plurality of groups of landmarks of a face of the patient is tracked, to obtain, for each group of the plurality of groups, one or more displacement measures characterizing positions of the landmarks of the group. The one or more displacement measures corresponding to each group are provided into a corresponding neural network, to obtain a landmark feature. The neural networks corresponding to each group are different from one another. A sequence score for the sequence is determined based on a plurality of landmark features corresponding to the groups. A plurality of sequence scores are provided into a machine learning component, to obtain a patient score. A disease state of the patient is determined based on the patient score.

Abstract: The pose of an implant represented in a medical image is determined from the medical image. The x-ray image of the implant is compared to a database of the implant viewed at different poses (e.g., viewed from different directions). The implant pose associated with the best match indicates the pose of the implant in the x-ray image.

Abstract: A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.

Abstract: The pose of an implant represented in a medical image is determined from the medical image. The x-ray image of the implant is compared to a database of the implant viewed at different poses (e.g., viewed from different directions). The implant pose associated with the best match indicates the pose of the implant in the x-ray image.

Abstract: A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.

Abstract: Automated detection of structure is provided in ultrasound M-mode imaging. A coarse and fine search for structure is used. For example, a less noise susceptible initial position or range of positions for a given structure is determined. This position is then refined. The coarse positioning and/or the refined position may use machine-trained classifiers. The positions of other structure may be used in either coarse or fine positioning, such as using a Markov Random Field. The structure or structures may be identified in the M-mode image without user input of a location in the M-mode image or along the line.

Abstract: Systems and methods for character-by-character alignment of two character sequences (such as OCR output from a scanned document and an electronic version of the same document) using a Hidden Markov Model (HMM) in a hierarchical fashion are disclosed. The method may include aligning two character sequences utilizing multiple hierarchical levels. For each hierarchical level above a final hierarchical level, the aligning may include parsing character subsequences from the two character sequences, performing an alignment of the character subsequences, and designating aligned character subsequences as the anchors, the parsing and performing the alignment being between the anchors generated from an immediately previous hierarchical level if the current hierarchical level is below the first hierarchical level. For the final hierarchical level, the aligning includes performing a character-by-character alignment of characters between anchors generated from the immediately previous hierarchical level.

Abstract: Automated detection of structure is provided in ultrasound M-mode imaging. A coarse and fine search for structure is used. For example, a less noise susceptible initial position or range of positions for a given structure is determined. This position is then refined. The coarse positioning and/or the refined position may use machine-trained classifiers. The positions of other structure may be used in either coarse or fine positioning, such as using a Markov Random Field. The structure or structures may be identified in the M-mode image without user input of a location in the M-mode image or along the line.