20,008 research outputs found
Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects
Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation
Autonomous harvesting and transportation is a long-term goal of the forest
industry. One of the main challenges is the accurate localization of both
vehicles and trees in a forest. Forests are unstructured environments where it
is difficult to find a group of significant landmarks for current fast
feature-based place recognition algorithms. This paper proposes a novel
approach where local observations are matched to a general tree map using the
Delaunay triangularization as the representation format. Instead of point cloud
based matching methods, we utilize a topology-based method. First, tree trunk
positions are registered at a prior run done by a forest harvester. Second, the
resulting map is Delaunay triangularized. Third, a local submap of the
autonomous robot is registered, triangularized and matched using triangular
similarity maximization to estimate the position of the robot. We test our
method on a dataset accumulated from a forestry site at Lieksa, Finland. A
total length of 2100\,m of harvester path was recorded by an industrial
harvester with a 3D laser scanner and a geolocation unit fixed to the frame.
Our experiments show a 12\,cm s.t.d. in the location accuracy and with
real-time data processing for speeds not exceeding 0.5\,m/s. The accuracy and
speed limit is realistic during forest operations
Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction
We present a method combining affinity prediction with region agglomeration,
which improves significantly upon the state of the art of neuron segmentation
from electron microscopy (EM) in accuracy and scalability. Our method consists
of a 3D U-NET, trained to predict affinities between voxels, followed by
iterative region agglomeration. We train using a structured loss based on
MALIS, encouraging topologically correct segmentations obtained from affinity
thresholding. Our extension consists of two parts: First, we present a
quasi-linear method to compute the loss gradient, improving over the original
quadratic algorithm. Second, we compute the gradient in two separate passes to
avoid spurious gradient contributions in early training stages. Our predictions
are accurate enough that simple learning-free percentile-based agglomeration
outperforms more involved methods used earlier on inferior predictions. We
present results on three diverse EM datasets, achieving relative improvements
over previous results of 27%, 15%, and 250%. Our findings suggest that a single
method can be applied to both nearly isotropic block-face EM data and
anisotropic serial sectioned EM data. The runtime of our method scales linearly
with the size of the volume and achieves a throughput of about 2.6 seconds per
megavoxel, qualifying our method for the processing of very large datasets
- …