406 research outputs found
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
Topology-Aware Loss for Aorta and Great Vessel Segmentation in Computed Tomography Images
Segmentation networks are not explicitly imposed to learn global invariants
of an image, such as the shape of an object and the geometry between multiple
objects, when they are trained with a standard loss function. On the other
hand, incorporating such invariants into network training may help improve
performance for various segmentation tasks when they are the intrinsic
characteristics of the objects to be segmented. One example is segmentation of
aorta and great vessels in computed tomography (CT) images where vessels are
found in a particular geometry in the body due to the human anatomy and they
mostly seem as round objects on a 2D CT image. This paper addresses this issue
by introducing a new topology-aware loss function that penalizes topology
dissimilarities between the ground truth and prediction through persistent
homology. Different from the previously suggested segmentation network designs,
which apply the threshold filtration on a likelihood function of the prediction
map and the Betti numbers of the ground truth, this paper proposes to apply the
Vietoris-Rips filtration to obtain persistence diagrams of both ground truth
and prediction maps and calculate the dissimilarity with the Wasserstein
distance between the corresponding persistence diagrams. The use of this
filtration has advantage of modeling shape and geometry at the same time, which
may not happen when the threshold filtration is applied. Our experiments on
4327 CT images of 24 subjects reveal that the proposed topology-aware loss
function leads to better results than its counterparts, indicating the
effectiveness of this use
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