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Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping
Deeply-Supervised CNN for Prostate Segmentation
Prostate segmentation from Magnetic Resonance (MR) images plays an important
role in image guided interven- tion. However, the lack of clear boundary
specifically at the apex and base, and huge variation of shape and texture
between the images from different patients make the task very challenging. To
overcome these problems, in this paper, we propose a deeply supervised
convolutional neural network (CNN) utilizing the convolutional information to
accurately segment the prostate from MR images. The proposed model can
effectively detect the prostate region with additional deeply supervised layers
compared with other approaches. Since some information will be abandoned after
convolution, it is necessary to pass the features extracted from early stages
to later stages. The experimental results show that significant segmentation
accuracy improvement has been achieved by our proposed method compared to other
reported approaches.Comment: Due to a crucial sign error in equation
Framing Apache Spark in life sciences
Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities
Annotating Synapses in Large EM Datasets
Reconstructing neuronal circuits at the level of synapses is a central
problem in neuroscience and becoming a focus of the emerging field of
connectomics. To date, electron microscopy (EM) is the most proven technique
for identifying and quantifying synaptic connections. As advances in EM make
acquiring larger datasets possible, subsequent manual synapse identification
({\em i.e.}, proofreading) for deciphering a connectome becomes a major time
bottleneck. Here we introduce a large-scale, high-throughput, and
semi-automated methodology to efficiently identify synapses. We successfully
applied our methodology to the Drosophila medulla optic lobe, annotating many
more synapses than previous connectome efforts. Our approaches are extensible
and will make the often complicated process of synapse identification
accessible to a wider-community of potential proofreaders
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
Guided Proofreading of Automatic Segmentations for Connectomics
Automatic cell image segmentation methods in connectomics produce merge and
split errors, which require correction through proofreading. Previous research
has identified the visual search for these errors as the bottleneck in
interactive proofreading. To aid error correction, we develop two classifiers
that automatically recommend candidate merges and splits to the user. These
classifiers use a convolutional neural network (CNN) that has been trained with
errors in automatic segmentations against expert-labeled ground truth. Our
classifiers detect potentially-erroneous regions by considering a large context
region around a segmentation boundary. Corrections can then be performed by a
user with yes/no decisions, which reduces variation of information 7.5x faster
than previous proofreading methods. We also present a fully-automatic mode that
uses a probability threshold to make merge/split decisions. Extensive
experiments using the automatic approach and comparing performance of novice
and expert users demonstrate that our method performs favorably against
state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at
http://rhoana.org/guidedproofreading/supplemental.pd
Integrated optical coherence tomography, ultrasound and photoacoustic imaging for ovarian tissue characterization
Ovarian cancer has the lowest survival rate of the gynecologic cancers because it is predominantly diagnosed in Stages III or IV due to the lack of reliable symptoms, as well as the lack of efficacious screening techniques. Detection before the malignancy spreads or at the early stage would greatly improve the survival and benefit patient health. In this report, we present an integrated optical coherence tomography (OCT), ultrasound (US) and photoacoustic imaging (PAI) prototype endoscopy system for ovarian tissue characterization. The overall diameter of the prototype endoscope is 5 mm which is suitable for insertion through a standard 5-12.5mm endoscopic laparoscopic port during minimally invasive surgery. It consists of a ball-lensed OCT sample arm probe, a multimode fiber having the output end polished at 45 degree angle so as to deliver the light perpendicularly for PAI, and a high frequency ultrasound transducer with 35MHz center frequency. System characterizations of OCT, US and PAI are presented. In addition, results obtained from ex vivo porcine and human ovarian tissues are presented. The optical absorption contrast provided by PAI, the high resolution subsurface morphology provided by OCT, and the deeper tissue structure imaged by US demonstrate the synergy of the combined endoscopy and the superior performance of this hybrid device over each modality alone in ovarian tissue characterization
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