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Clifton Park, NY, United States

Kitware, Inc. is a technology company headquartered in Clifton Park, New York. The company specializes in the research and development of open-source software in the fields of computer vision, medical imaging, visualization, 3D data publishing and technical software development. In addition to software development, the company offers other products and services such as books, technical support, consulting and customized training courses. Wikipedia.

Leotta M.J.,Kitware | Mundy J.L.,Brown University
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2011

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task. © 2011 IEEE. Source

Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

DESCRIPTION provided by applicant Prostate cancer PCA is the most common non skin cancer and the second leading cause of cancer death in American men with over new cases diagnosed and over PCA deaths annually in the United States Ultrasound guided biopsy is the standard of care for confirming cancer typically following elevated PSA levels however it has been estimated that up to of men require three or more biopsy sessions for diagnosis and biopsies have a high risk of hemorrhage and infection Furthermore biopsies are not sufficient for tumor delineation or characterization because they sparsely sample the entire organ Regretfully the deficiencies of biopsy have lead to over treatment of indolent disease with radical prostatectomies a drastic treatment that has significant risks of infection hemorrhage urinary incontinence and impotence We are the inventors of Acoustic Radiation Force Impulse ARFI imaging and the inventors of Shear Wave Elasticity Imaging SWEI methods We have now combined those methods into ARFI SWEI imaging sequences that define a new and novel multi parametric ultrasonic elasticity imaging system This multi parametric elasticity imaging approach provides an absolute quantitative measure of tissue stiffness at high resolution in D using ultrasound We hypothesize that synergistic diagnostic information from B mode ARFI SWEI and multi parametric MRI mpMRI e g diffusion weighted imaging and MR spectroscopy imaging enable a the sensitive and specific diagnosis of PCA and b the accurate delineation of PCA margins We propose retrospective studies on existing D B mode ARFI SWEI and mpMRI imaging datasets to test this hypothesis PUBLIC HEALTH RELEVANCE Prostate cancer is the second leading cause of cancer death in American men with over new cases and over deaths annually We have developed a novel ultrasonic elasticity imaging technique called Acoustic Radiation Force Impulse Shear Wave Elasticity Imaging ARFI SWEI that is an enhanced multi parametric version of ultrasound imaging We propose to test ARFI SWEIandapos s ability to replace invasive and risky biopsies for PCA diagnosis and focal treatment planning

Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 1.12M | Year: 2014

ABSTRACT: ISR analysts are faced with severe challenges due to the volume of data, the speed at which it arrives, and the variety in sensor signatures and scene content. FMV and WAMI analysts face a fire hose of pixel data. Kitware has developed significant capabilities for ISR video exploitation, with a focus on understanding the function of objects and regions in the scene. These function-based exploitation tools can provide significant help in the automation of both real-time and forensic ISR video exploitation. We plan to leverage and enhance these capabilities to help further advance the state of the art in exploitation technologies. The development and transition of these technologies to operational use could significantly improve the quality of intelligence derived from video streams as well as reduce the resources, both time and personnel, necessary to extract critical intelligence. In terms of commercial opportunities, there are large system integrators who provide FMV exploitation workstations, including Leidos"AIMES system and General Dynamics MAAS systems to the government. Kitware has already received a Phase III SBIR to start transition of our FMV capabilities into the AIMES system. BENEFIT: The primary benefit of the proposed work is a solution to the ISR analyst data-workload challenge. ISR analysts are faced with severe challenges due to the volume of data, the speed at which it arrives, and the variety in sensor signatures and scene content. There are not enoughWAMI analysts to view and exploit all the pixels currently produced by the sensor, even in forensic exploitation. The proposed technology will benefit military analysts on the ground who are responsible for gathering, analyzing, and acting on intelligence, a critical component of almost every mission, though the use of automatic, function-based object detection. The proposed technology will reduce analyst workload while simultaneously increasing the amount of data that can be analyzed, improving security and intelligence gathering. Additionally, there are commercial applications of the technology that provide public benefit and commercial opportunities. These opportunities include the integration of the technology with unmanned aerial systems (UAS) in the commercial sector to benefit precision agriculture; security and monitoring for public safety; exploration, aid efforts, and disaster recovery; and environmental monitoring.

Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 635.67K | Year: 2015

DESCRIPTION provided by applicant Nearly million Americans suffer traumatic brain injury TBI annually which constitutes a significant US medical health concern Although neuroimaging plays an important role in pathology localization and surgical planning TBI clinical care does not currently take full advantage of neuroimaging computational technology We propose to develop validate and commercialize computational algorithms based on our methods for image segmentation and registration These methods can accommodate the presence of large pathologies in TBI cases can yield quantitative measures from chronic and acute TBI data for research into characterizing injury monitoring pathology evolution informing patient prognosis and can aid clinicians in optimizing TBI patient care workflows We will accomplish our goal during the proposed Phase II effort by building upon our Phase I successes Featured in conference and journal publications during Phase I we devised a novel andquot low rank sparseandquot method for registering brain MRI scans from TBI patients with large pathologies to healthy brain atlases enabling more accurate identification and quantification of anatomic changes In conjunction with our foundational andquot geometric metamorphosisandquot work into quantifying lesion infiltration and recession over time our set of methods now address the major hurdles associated with TBI patient understanding Under this Phase II STTR proposal we will specifically focus on extending our computational methods for multimodal neuroimaging of TBI data processing We will provide finite element models created over a range of clinical cases of mild to severe TBI determine refined measures of patient change from longitudinal registrations integrate those methods into local and cloud based environments that support academic and commercial use and validate the complete commercial system using extensive TBI data collections including neuropsychological motor cognitive and behavioral outcome measures in a customer oriented study PUBLIC HEALTH RELEVANCE In the US approximately million individuals are victims of traumatic brain injury TBI annually with many requiring surgical intervention or long term care Initial assessment and treatment of TBI have appropriately become major US healthcare initiatives yet the effects of TBI can be particularly challenging for the patient and for healthcre systems Neuroimaging data analysis methods however are presently not properly employed to address this challenge Herein we propose to refine apply and test tools initiated under our Phase I STTR to perform the combined efficient analysis of multimodal neuroimage data sets for use in assessing the extent of brain injury its change over time and its effective treatment

Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 1.26M | Year: 2015

DESCRIPTION provided by applicant There are several features of ultrasound imaging that make it attractive to clinicians and preclinical researchers including its relatively low cost rel time imaging capability safety and portability For example ultrasound imaging is widely used for anatomical imaging and blood flow measurements in the heart and large vessels Ultrasound however is typically not used in oncology because it has relatively poor quantitative capability with respect to tumor morphology or malignancy In our Phase I work we demonstrated that our new contrast enhanced dual frequency ultrasound technologies andquot Acoustic Angiographyandquot can capture detailed in vivo images of tissue vasculature in animal models of breast cancer and we have shown that vascular morphology is an indicator of tumor malignancy in those animal models That work built upon our prior vessel analysis research and algorithms that showed that quantifiable vascular morphology metrics from Magnetic Resonance Imaging data are reliable predictors of tumor malignancy and response to therapy in humans In the proposed Phase II work we will conduct the research necessary for the commercialization of our acoustic angiography system for preclinical research The team has been expanded to include SonoVol the manufacturer of ultrasound systems for preclinical research We will research and evaluate methods to ensure that our commercial system will be easy to use and consistently produce effective measures of tumor malignancy and response to therapy We will validate the product in a blinded study of breast cancer treatment efficacy in support of a preclinical trial i e the targeted commercial use for the proposed system PUBLIC HEALTH RELEVANCE Ultrasound is a relatively safe low cost portable real time imaging device however its images are relatively poor for detecting and diagnosing tumors We propose that ultrasound can be extended to tumor assessment via a commercial system that combines new micro bubble contrast agents that enhance the appearance of vessels within ultrasound images with an ultrasound imaging probe that we developed for capturing contrast enhanced ultrasound images and with novel vascular image analysis algorithms that we have also developed In Phase I we showed that our system is viable In the proposed Phase II work we will conduct the research and development needed to commercialize that system for assessing tumor malignancy and response to treatment in support of preclinical trials of experimental cancer therapies

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