William Gandler

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Imaging has become an essential component in many jields of’ medical and laboratory research and clinical practice. Biologists s t t i 4 cells and generate 3 0 confocal microscopy da fasets, virologists generate 3 0 reconstructions of’ viruses jrom micrographs, radiologists idcnt{b cind qimntib tumors ,finm MRI ond CT scans, rind nenroscientists detect(More)
We describe a new collection of publicly available software tools for performing quantitative neuroimage analysis. The tools perform semi-automatic brain extraction, tissue classification, Talairach alignment, and atlas-based measurements within a user-friendly graphical environment. They are implemented as plug-ins for MIPAV, a freely available medical(More)
OBJECTIVE The aim of this study was to evaluate a 3D tumor segmentation method for fluorodeoxyglucose positron emission tomography (FDG-PET) in the context of noninvasive estimation of tumor metabolic length (Lm), as it correlates with surgical pathology and phantom results. METHODS Thirty-four patients (7 women, 27 men) with esophageal cancer were(More)
Prostate segmentation on 3D MR images is a challenging task due to image artifacts, large inter-patient prostate shape and texture variability, and lack of a clear prostate boundary specifically at apex and base levels. We propose a supervised machine learning model that combines atlas based Active Appearance Model (AAM) with a Deep Learning model to(More)
We describe the construction and use of a compact dual-view inverted selective plane illumination microscope (diSPIM) for time-lapse volumetric (4D) imaging of living samples at subcellular resolution. Our protocol enables a biologist with some prior microscopy experience to assemble a diSPIM from commercially available parts, to align optics and test(More)
Automatic MR whole prostate segmentation is a challenging task. Recent approaches have attempted to harness the capabilities of deep learning for MR prostate segmentation to tackle pixel-level labeling tasks. Patch-based and hierarchical features-based deep CNN models were used to delineate the prostate boundary. To further investigate this problem, we(More)
Logarithmic amplifiers (log amps), which produce an output signal proportional to the logarithm of the input signal, are widely used in cytometry for measurements of parameters that vary over a wide dynamic range, e.g., cell surface immunofluorescence. Existing log amp circuits all deviate to some extent from ideal performance with respect to dynamic range(More)
Automatic prostate segmentation in MR images is a challenging task due to inter-patient prostate shape and texture variability, and the lack of a clear prostate boundary. We propose a supervised learning framework that combines the atlas based AAM and SVM model to achieve a relatively high segmentation result of the prostate boundary. The performance of the(More)
Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a challenging task due to the high variability of prostate anatomic structure. Artifacts such as noise and similar signal intensity of tissues around the prostate boundary inhibit traditional segmentation methods from achieving high accuracy. We investigate both(More)