Progressive data transmission for anatomical landmark detection in a cloud.

BACKGROUND: In the concept of cloud-computing-based systems, various authorized users have secure access to patient records from a number of care delivery organizations from any location. This creates a growing need for remote visualization, advanced image processing, state-of-the-art image analysis, and computer aided diagnosis. OBJECTIVES: This paper proposes a system of algorithms for automatic detection of anatomical landmarks in 3D volumes in the cloud computing environment. The system addresses the inherent problem of limited bandwidth between a (thin) client, data center, and data analysis server. METHODS: The problem of limited bandwidth is solved by a hierarchical sequential detection algorithm that obtains data by progressively transmitting only image regions required for processing. The client sends a request to detect a set of landmarks for region visualization or further analysis. The algorithm running on the data analysis server obtains a coarse level image from the data center and generates landmark location candidates. The candidates are then used to obtain image neighborhood regions at a finer resolution level for further detection. This way, the landmark locations are hierarchically and sequentially detected and refined. RESULTS: Only image regions surrounding landmark location candidates need to be trans- mitted during detection. Furthermore, the image regions are lossy compressed with JPEG 2000. Together, these properties amount to at least 30 times bandwidth reduction while achieving similar accuracy when compared to an algorithm using the original data. CONCLUSIONS: The hierarchical sequential algorithm with progressive data transmission considerably reduces bandwidth requirements in cloud-based detection systems.

Towards patient-specific finite-element simulation of MitralClip procedure.

MitralClip is a novel minimally invasive procedure to treat mitral valve (MV) regurgitation. It consists in clipping the mitral leaflets together to close the regurgitant hole. A careful preoperative planning is necessary to select respondent patients and to determine the clipping sites. Although preliminary indications criteria are established, they lack prediction power with respect to complications and effectiveness of the therapy in specific patients. We propose an integrated framework for personalized simulation of MV function and apply it to simulate MitralClip procedure. A patient-specific dynamic model of the MV apparatus is computed automatically from 4D TEE images. A biomechanical model of the MV, constrained by the observed motion of the mitral annulus and papillary muscles, is employed to simulate valve closure and MitralClip intervention. The proposed integrated framework enables, for the first time, to quantitatively evaluate an MV finite-element model in-vivo, on eleven patients, and to predict the outcome of MitralClip intervention in one of these patients. The simulations are compared to ground truth and to postoperative images, resulting in promising accuracy (average point-to-mesh distance: 1.47 +/- 0.24 mm). Our framework may constitute a tool for MV therapy planning and patient management.

Computer-assisted discrimination among malignant lymphomas and leukemia using immunophenotyping, intelligent image repositories, and telemicroscopy.

The process of discriminating among pathologies involving peripheral blood, bone marrow, and lymph node has traditionally begun with subjective morphological assessment of cellular materials viewed using light microscopy. The subtle visible differences exhibited by some malignant lymphomas and leukemia, however, give rise to a significant number of false negatives during microscopic evaluation by medical technologists. We have developed a distributed, clinical decision support prototype for distinguishing among hematologic malignancies. The system consists of two major components, a distributed telemicroscopy system and an intelligent image repository. The hybrid system enables individuals located at disparate clinical and research sites to engage in interactive consultation and to obtain computer-assisted decision support. Software, written in JAVA, allows primary users to control the specimen stage, objective lens, light levels, and focus of a robotic microscope remotely while a digital representation of the specimen is continuously broadcast to all session participants. Primary user status can be passed as a token. The system features shared graphical pointers, text messaging capability, and automated database management. Search engines for the database allow one to automatically identify and retrieve images, diagnoses, and correlated clinical data of cases from a "gold standard" database which exhibit spectral and spatial profiles which are most similar to a given query image. The system suggests the most likely diagnosis based on majority logic of the retrieved cases. The system was used to discriminate among three lymphoproliferative disorders and healthy cells. The system provided the correct classification in more than 83% of the cases studied. System performance was evaluated using rigorous statistical assessment and by comparison with human observers.