Biology subjects

Sapiro, G.

Publications and source records attributed to Sapiro, G..

5 recordsLinked to original sources

bioRxiv2018

Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope

GoalIn this work, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugols iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance.\n\nMethodsWe developed algorithms to pre-process pathology-labeled cervigrams and to extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugols iodine, and a combination of the two contrasts.\n\nResultsThe proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, 63% accuracy).\n\nConclusionThe results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugols iodine images may provide unbiased automation of cervigrams.\n\nSignificanceThis would enable automated, expert-level diagnosis of cervical pre-cancer at the point-of-care.

bioinformatics↗

bioRxiv2018

Automatic Localization of the Subthalamic Nucleus on Patient-Specific Clinical MRI by Incorporating 7T MRI and Machine Learning: Application in Deep Brain Stimulation

Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinsons disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

neuroscience↗

bioRxiv2018

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

ObjectiveOverlapping measures are often utilized to quantify the similarity between two binary regions. However, modern segmentation algorithms output a probability or confidence map with continuous values in the zero-to-one interval. Moreover, these binary overlapping measures are biased to structures size. Addressing these challenges is the objective of this work.\n\nMethodsWe extend the definition of the classical Dice coefficient (DC) overlap to facilitate the direct comparison of a ground truth binary image with a probabilistic map. We call the extended method continuous Dice coefficient (cDC) and show that 1) cDC [≤]1 and cDC = 1 if-and-only-if the structures overlap is complete, and; 2) cDC is monotonically decreasing with the amount of overlap. We compare the classical DC and the cDC in a simulation of partial volume effects that incorporates segmentations of common targets for deep-brain-stimulation. Lastly, we investigate the cDC for an automatic segmentation of the subthalamic-nucleus.\n\nResultsPartial volume effect simulation on thalamus (large structure) resulted with DC and cDC averages (SD) of 0.98 (0.006) and 0.99 (0.001), respectively. For subthalamic-nucleus (small structure) DC and cDC were 0.86 (0.025) and 0.97 (0.006), respectively. The DC and cDC for automatic STN segmentation were 0.66 and 0.80, respectively.\n\nConclusionThe cDC is well defined for probabilistic segmentation, less biased to structures size and more robust to partial volume effects in comparison to DC. Significance: The proposed method facilitates a better evaluation of segmentation algorithms. As a better measurement tool, it opens the door for the development of better segmentation methods.

bioengineering↗

bioRxiv2018

Active Learning of Cortical Connectivity from Two-Photon Imaging Data

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network.. Unlike existing system identification techniques, this \"active learning\" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

neuroscience↗

bioRxiv2018

Automated Antibody Characterization and Screening for Array Tomography via Probabilistic Synapse Detection

Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Screening Tool (SACT), is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. By allowing the user to define the molecular composition and size of synapses expected to contain the antigen, the method detects and characterizes puncta and synapses, and outputs automatically computed characteristics such as synapse density and target specificity ratio, which reflect the sensitivity and specificity of immunolabeling with a given antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.

neuroscience↗