19 research outputs found
CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation
The detection of curvilinear structures in medical images, e.g., blood vessels or nerve fibers, is important in aiding management of many diseases. In this work, we propose a general unifying curvilinear structure segmentation network that works on different medical imaging modalities: optical coherence tomography angiography (OCT-A), color fundus image, and corneal confocal microscopy (CCM). Instead of the U-Net based convolutional neural network, we propose a novel network (CS-Net) which includes a self-attention mechanism in the encoder and decoder. Two types of attention modules are utilized - spatial attention and channel attention, to further integrate local features with their global dependencies adaptively. The proposed network has been validated on five datasets: two color fundus datasets, two corneal nerve datasets and one OCT-A dataset. Experimental results show that our method outperforms state-of-the-art methods, for example, sensitivities of corneal nerve fiber segmentation were at least 2% higher than the competitors. As a complementary output, we made manual annotations of two corneal nerve datasets which have been released for public access
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
An Elastic Interaction-Based Loss Function for Medical Image Segmentation
Deep learning techniques have shown their success in medical image
segmentation since they are easy to manipulate and robust to various types of
datasets. The commonly used loss functions in the deep segmentation task are
pixel-wise loss functions. This results in a bottleneck for these models to
achieve high precision for complicated structures in biomedical images. For
example, the predicted small blood vessels in retinal images are often
disconnected or even missed under the supervision of the pixel-wise losses.
This paper addresses this problem by introducing a long-range elastic
interaction-based training strategy. In this strategy, convolutional neural
network (CNN) learns the target region under the guidance of the elastic
interaction energy between the boundary of the predicted region and that of the
actual object. Under the supervision of the proposed loss, the boundary of the
predicted region is attracted strongly by the object boundary and tends to stay
connected. Experimental results show that our method is able to achieve
considerable improvements compared to commonly used pixel-wise loss functions
(cross entropy and dice Loss) and other recent loss functions on three retinal
vessel segmentation datasets, DRIVE, STARE and CHASEDB1
The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation
We propose an encoder-decoder framework for the segmentation of blood vessels
in retinal images that relies on the extraction of large-scale patches at
multiple image-scales during training. Experiments on three fundus image
datasets demonstrate that this approach achieves state-of-the-art results and
can be implemented using a simple and efficient fully-convolutional network
with a parameter count of less than 0.8M. Furthermore, we show that this
framework - called VLight - avoids overfitting to specific training images and
generalizes well across different datasets, which makes it highly suitable for
real-world applications where robustness, accuracy as well as low inference
time on high-resolution fundus images is required
Animals in biomedical research : towards a more compassionate approach
The development of novel biosensors and other medical devices often
implies extensive animal testing before the final phases of product
development are reached. Most products do not reach the market. On the
other hand, natural disease in companion animals often mirrors disease
processes in humans, being a more accurate representation of the
complexity of disease processes than laboratory animals. These are poor models of genetic variability, environmental exposure influence and often,
pathophysiology.
In this chapter, possible strategies for biomedical research that are
based on a more compassionate approach are discussed, tapping on the
enormous potential of comparative medicine for beneficing both humans
and animals.5F1C-9D14-387E | Joana Margarida Ferreira da Costa Reisinfo:eu-repo/semantics/publishedVersio
Automated Detection of Leakage in Fluorescein Angiography Images with Application to Malarial Retinopathy
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.NSF (Grants 1231216, 1212849 and 1447476)ONR MURI (Grant N00014-16-1-2007