29 research outputs found
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We propose a novel convolutional neural network for lesion detection from
weak labels. Only a single, global label per image - the lesion count - is
needed for training. We train a regression network with a fully convolutional
architecture combined with a global pooling layer to aggregate the 3D output
into a scalar indicating the lesion count. When testing on unseen images, we
first run the network to estimate the number of lesions. Then we remove the
global pooling layer to compute localization maps of the size of the input
image. We evaluate the proposed network on the detection of enlarged
perivascular spaces in the basal ganglia in MRI. Our method achieves a
sensitivity of 62% with on average 1.5 false positives per image. Compared with
four other approaches based on intensity thresholding, saliency and class maps,
our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the
first version: padding, loss, typos and update of the DOI numbe
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Intracranial carotid artery calcification (ICAC) is a major risk factor for
stroke, and might contribute to dementia and cognitive decline. Reliance on
time-consuming manual annotation of ICAC hampers much demanded further research
into the relationship between ICAC and neurological diseases. Automation of
ICAC segmentation is therefore highly desirable, but difficult due to the
proximity of the lesions to bony structures with a similar attenuation
coefficient. In this paper, we propose a method for automatic segmentation of
ICAC; the first to our knowledge. Our method is based on a 3D fully
convolutional neural network that we extend with two regularization techniques.
Firstly, we use deep supervision (hidden layers supervision) to encourage
discriminative features in the hidden layers. Secondly, we augment the network
with skip connections, as in the recently developed ResNet, and dropout layers,
inserted in a way that skip connections circumvent them. We investigate the
effect of skip connections and dropout. In addition, we propose a simple
problem-specific modification of the network objective function that restricts
the focus to the most important image regions and simplifies the optimization.
We train and validate our model using 882 CT scans and test on 1,000. Our
regularization techniques and objective improve the average Dice score by 7.1%,
yielding an average Dice of 76.2% and 97.7% correlation between predicted ICAC
volumes and manual annotations.Comment: Accepted for MICCAI 201
3D Regression Neural Network for the Quantification of Enlarged Perivascular Spaces in Brain MRI
Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging
marker for cerebral small vessel disease, and have been shown to be related to
increased risk of various neurological diseases, including stroke and dementia.
Automatic quantification of EPVS would greatly help to advance research into
its etiology and its potential as a risk indicator of disease. We propose a
convolutional network regression method to quantify the extent of EPVS in the
basal ganglia from 3D brain MRI. We first segment the basal ganglia and
subsequently apply a 3D convolutional regression network designed for small
object detection within this region of interest. The network takes an image as
input, and outputs a quantification score of EPVS. The network has
significantly more convolution operations than pooling ones and no final
activation, allowing it to span the space of real numbers. We validated our
approach using a dataset of 2000 brain MRI scans scored visually. Experiments
with varying sizes of training and test sets showed that a good performance can
be achieved with a training set of only 200 scans. With a training set of 1000
scans, the intraclass correlation coefficient (ICC) between our scoring method
and the expert's visual score was 0.74. Our method outperforms by a large
margin - more than 0.10 - four more conventional automated approaches based on
intensities, scale-invariant feature transform, and random forest. We show that
the network learns the structures of interest and investigate the influence of
hyper-parameters on the performance. We also evaluate the reproducibility of
our network using a set of 60 subjects scanned twice (scan-rescan
reproducibility). On this set our network achieves an ICC of 0.93, while the
intrarater agreement reaches 0.80. Furthermore, the automatic EPVS scoring
correlates similarly to age as visual scoring
Deep Learning from Label Proportions for Emphysema Quantification
We propose an end-to-end deep learning method that learns to estimate
emphysema extent from proportions of the diseased tissue. These proportions
were visually estimated by experts using a standard grading system, in which
grades correspond to intervals (label example: 1-5% of diseased tissue). The
proposed architecture encodes the knowledge that the labels represent a
volumetric proportion. A custom loss is designed to learn with intervals. Thus,
during training, our network learns to segment the diseased tissue such that
its proportions fit the ground truth intervals. Our architecture and loss
combined improve the performance substantially (8% ICC) compared to a more
conventional regression network. We outperform traditional lung densitometry
and two recently published methods for emphysema quantification by a large
margin (at least 7% AUC and 15% ICC), and achieve near-human-level performance.
Moreover, our method generates emphysema segmentations that predict the spatial
distribution of emphysema at human level.Comment: Accepted to MICCAI 201
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large
quantity of labeled data. This problem is even more challenging in medical
image analysis where the annotator expertise is often scarce. We propose a
novel data-augmentation method to regularize neural network regressors that
learn from a single global label per image. The principle of the method is to
create new samples by recombining existing ones. We demonstrate the performance
of our algorithm on two tasks: estimation of the number of enlarged
perivascular spaces in the basal ganglia, and estimation of white matter
hyperintensities volume. We show that the proposed method improves the
performance over more basic data augmentation. The proposed method reached an
intraclass correlation coefficient between ground truth and network predictions
of 0.73 on the first task and 0.84 on the second task, only using between 25
and 30 scans with a single global label per scan for training. With the same
number of training scans, more conventional data augmentation methods could
only reach intraclass correlation coefficients of 0.68 on the first task, and
0.79 on the second task.Comment: accepted in MICCAI 201
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Scoliosis is a condition defined by an abnormal spinal curvature. For
diagnosis and treatment planning of scoliosis, spinal curvature can be
estimated using Cobb angles. We propose an automated method for the estimation
of Cobb angles from X-ray scans. First, the centerline of the spine was
segmented using a cascade of two convolutional neural networks. After smoothing
the centerline, Cobb angles were automatically estimated using the derivative
of the centerline. We evaluated the results using the mean absolute error and
the average symmetric mean absolute percentage error between the manual
assessment by experts and the automated predictions. For optimization, we used
609 X-ray scans from the London Health Sciences Center, and for evaluation, we
participated in the international challenge "Accurate Automated Spinal
Curvature Estimation, MICCAI 2019" (100 scans). On the challenge's test set, we
obtained an average symmetric mean absolute percentage error of 22.96
DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data
Blood vessels of the brain are providing the human brain with the required
nutrients and oxygen. As a vulnerable part of the cerebral blood supply,
pathology of small vessels can cause serious problems such as Cerebral Small
Vessel Diseases (CSVD). It has also been shown that CSVD is related to
neurodegeneration, such as in Alzheimer's disease. With the advancement of 7
Tesla MRI systems, higher spatial image resolution can be achieved, enabling
the depiction of very small vessels in the brain. Non-Deep Learning based
approaches for vessel segmentation, e.g. Frangi's vessel enhancement with
subsequent thresholding are capable of segmenting medium to large vessels but
often fail to segment small vessels. The sensitivity of these methods to small
vessels can be increased by extensive parameter tuning or by manual
corrections, albeit making them time-consuming, laborious, and not feasible for
larger datasets. This paper proposes a deep learning architecture to
automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic
Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a
small imperfect semi-automatically segmented dataset of only 11 subjects; using
six for training, two for validation and three for testing. Deep learning model
based on U-Net Multi-Scale Supervision was trained using the training subset
and were made equivariant to elastic deformations in a self-supervised manner
using deformation-aware learning to improve the generalisation performance. The
proposed technique was evaluated quantitatively and qualitatively against the
test set and achieved a dice score of 80.440.83. Furthermore, the result
of the proposed method was compared against a selected manually segmented
region (62.07 resultant dice) and has shown a considerable improvement (18.98%)
with deformation-aware learning
Adversarial Attack Vulnerability of Medical Image Analysis Systems: Unexplored Factors
Adversarial attacks are considered a potentially serious security threat for
machine learning systems. Medical image analysis (MedIA) systems have recently
been argued to be vulnerable to adversarial attacks due to strong financial
incentives and the associated technological infrastructure.
In this paper, we study previously unexplored factors affecting adversarial
attack vulnerability of deep learning MedIA systems in three medical domains:
ophthalmology, radiology, and pathology. We focus on adversarial black-box
settings, in which the attacker does not have full access to the target model
and usually uses another model, commonly referred to as surrogate model, to
craft adversarial examples. We consider this to be the most realistic scenario
for MedIA systems.
Firstly, we study the effect of weight initialization (ImageNet vs. random)
on the transferability of adversarial attacks from the surrogate model to the
target model. Secondly, we study the influence of differences in development
data between target and surrogate models. We further study the interaction of
weight initialization and data differences with differences in model
architecture. All experiments were done with a perturbation degree tuned to
ensure maximal transferability at minimal visual perceptibility of the attacks.
Our experiments show that pre-training may dramatically increase the
transferability of adversarial examples, even when the target and surrogate's
architectures are different: the larger the performance gain using
pre-training, the larger the transferability. Differences in the development
data between target and surrogate models considerably decrease the performance
of the attack; this decrease is further amplified by difference in the model
architecture. We believe these factors should be considered when developing
security-critical MedIA systems planned to be deployed in clinical practice.Comment: First three authors contributed equall