21 research outputs found
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
Promoting Sustainability through Next-Generation Biologics Drug Development
The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using âdigital twinsâ can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organizationâs 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.BMBF, 01DD20002A, Verbundprojekt: Internationales Zukunftslabor fĂŒr KI-gestĂŒtzte Bioprozessentwicklung "KIWI-biolab"; Teilvorhaben: Koordination und Aufbau eines KI-Exzellenzzentrum
SlicerTMS: Interactive Real-time Visualization of Transcranial Magnetic Stimulation using Augmented Reality and Deep Learning
Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation
approach that effectively treats various brain disorders. One of the critical
factors in the success of TMS treatment is accurate coil placement, which can
be challenging, especially when targeting specific brain areas for individual
patients. Calculating the optimal coil placement and the resulting electric
field on the brain surface can be expensive and time-consuming. We introduce
SlicerTMS, a simulation method that allows the real-time visualization of the
TMS electromagnetic field within the medical imaging platform 3D Slicer. Our
software leverages a 3D deep neural network, supports cloud-based inference,
and includes augmented reality visualization using WebXR. We evaluate the
performance of SlicerTMS with multiple hardware configurations and compare it
against the existing TMS visualization application SimNIBS. All our code, data,
and experiments are openly available:
\url{https://github.com/lorifranke/SlicerTMS}Comment: 11 pages, 3 figures, 2 tables, MICCA
How Machine Learning is Powering Neuroimaging to Improve Brain Health
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, âNeuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Applicationâ, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health