The State University of New York

Yi Gao

Wei Zhu

Tianhao Zhao

Tahsin M Kurc

Joel Haskin Saltz

Si Wen

Sampurna Shrestha

A method of testing an impedance-sensitive system with a switching device, wherein the method comprises: switching the disconnecting device into the on-state configured to permit transmission of energy via the coil; implementing a first measurement with the impedance-sensitive system; switching the disconnecting device into the off-state configured to permit damping of the external positioning signal that couples into the coil so as to reduce the undesirable oscillations of the coil; implementing a second measurement with the impedance-sensitive system; performing a comparison of the first measurement and the second measurement; performing a verification of the comparison with a target specification; and displaying a correct function and/or a malfunction depending on the verification.

System and method associated with predicting segmentation quality of objects in analysis of copious image data

A system associated with predicting segmentation quality of segmented objects implemented in the analysis of copious image data is disclosed. The system receives a collection of image data related to a particular type of data. The image data is segmented into segmented data portions based on an object associated with the collection of image data. Regions of interest associated with the segmented data portions are determined. The quality of segmentation of the segmented data portions is determined for respective classification of the regions of interest. A classification label is assigned to the regions of interest. Regions of interest are partitioned into sub-regions. Features associated with the sub-regions of the segmented data portions are determined. A training dataset is generated based on the determined features associated with the sub-regions in order to train a classification model based on a predetermined threshold value. Test images are received to iteratively classify segmented data portions based on an object associated with the test images, using the trained classification model. The segmentation quality of segmented objects in the test images is predicted based on the trained classification model. A corresponding method and computer-readable device are also disclosed.

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Stony Brook, NY, United States of America

Yi Gao