47 research outputs found
Super-Resolution From Binary Measurements With Unknown Threshold
We address the problem of super-resolution of point sources from binary
measurements, where random projections of the blurred measurement of the actual
signal are encoded using only the sign information. The threshold used for
binary quantization is not known to the decoder. We develop an algorithm that
solves convex programs iteratively and achieves signal recovery. The proposed
algorithm, which we refer to as the binary super-resolution (BSR) algorithm,
recovers point sources with reasonable accuracy, albeit up to a scale factor.
We show through simulations that the BSR algorithm is successful in recovering
the locations and the amplitudes of the point sources, even in the presence of
significant amount of blurring. We also propose a framework for handling noisy
measurements and demonstrate that BSR gives a reliable reconstruction
(correspondingly, reconstruction signal-to-noise ratio (SNR) of about 22 dB)
for a measurement SNR of 15 dB
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
Multi-class segmentation of vertebrae is a non-trivial task mainly due to the
high correlation in the appearance of adjacent vertebrae. Hence, such a task
calls for the consideration of both global and local context. Based on this
motivation, we propose a two-staged approach that, given a computed tomography
dataset of the spine, segments the five lumbar vertebrae and simultaneously
labels them. The first stage employs a multi-layered perceptron performing
non-linear regression for locating the lumbar region using the global context.
The second stage, comprised of a fully-convolutional deep network, exploits the
local context in the localised lumbar region to segment and label the lumbar
vertebrae in one go. Aided with practical data augmentation for training, our
approach is highly generalisable, capable of successfully segmenting both
healthy and abnormal vertebrae (fractured and scoliotic spines). We
consistently achieve an average Dice coefficient of over 90 percent on a
publicly available dataset of the xVertSeg segmentation challenge of MICCAI
2016. This is particularly noteworthy because the xVertSeg dataset is beset
with severe deformities in the form of vertebral fractures and scoliosis
A Relational-learning Perspective to Multi-label Chest X-ray Classification
Multi-label classification of chest X-ray images is frequently performed
using discriminative approaches, i.e. learning to map an image directly to its
binary labels. Such approaches make it challenging to incorporate auxiliary
information such as annotation uncertainty or a dependency among the labels.
Building towards this, we propose a novel knowledge graph reformulation of
multi-label classification, which not only readily increases predictive
performance of an encoder but also serves as a general framework for
introducing new domain knowledge.
Specifically, we construct a multi-modal knowledge graph out of the chest
X-ray images and its labels and pose multi-label classification as a link
prediction problem. Incorporating auxiliary information can then simply be
achieved by adding additional nodes and relations among them. When tested on a
publicly-available radiograph dataset (CheXpert), our relational-reformulation
using a naive knowledge graph outperforms the state-of-art by achieving an
area-under-ROC curve of 83.5%, an improvement of "sim 1" over a purely
discriminative approach
Deep Reinforcement Learning for Organ Localization in CT
Robust localization of organs in computed tomography scans is a constant
pre-processing requirement for organ-specific image retrieval, radiotherapy
planning, and interventional image analysis. In contrast to current solutions
based on exhaustive search or region proposals, which require large amounts of
annotated data, we propose a deep reinforcement learning approach for organ
localization in CT. In this work, an artificial agent is actively self-taught
to localize organs in CT by learning from its asserts and mistakes. Within the
context of reinforcement learning, we propose a novel set of actions tailored
for organ localization in CT. Our method can use as a plug-and-play module for
localizing any organ of interest. We evaluate the proposed solution on the
public VISCERAL dataset containing CT scans with varying fields of view and
multiple organs. We achieved an overall intersection over union of 0.63, an
absolute median wall distance of 2.25 mm, and a median distance between
centroids of 3.65 mm.Comment: Accepted paper in MIDL 202
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
Purpose: To use and test a labelling algorithm that operates on
two-dimensional (2D) reformations, rather than three-dimensional (3D) data to
locate and identify vertebrae.
Methods: We improved the Btrfly Net (described by Sekuboyina et al) that
works on sagittal and coronal maximum intensity projections (MIP) and augmented
it with two additional components: spine-localization and adversarial a
priori-learning. Furthermore, we explored two variants of adversarial training
schemes that incorporated the anatomical a priori knowledge into the Btrfly
Net. We investigated the superiority of the proposed approach for labelling
vertebrae on three datasets: a public benchmarking dataset of 302 CT scans and
two in-house datasets with a total of 238 CT scans. We employed Wilcoxon
signed-rank test to compute the statistical significance of the improvement in
performance observed due to various architectural components in our approach.
Results: On the public dataset, our approach using the described
Btrfly(pe-eb) network performed on par with current state-of-the-art methods
achieving a statistically significant (p < .001) vertebrae identification rate
of 88.5+/-0.2 % and localization distances of less than 7-mm. On the in-house
datasets that had a higher inter-scan data variability, we obtained an
identification rate of 85.1+/-1.2%.
Conclusion: An identification performance comparable to existing 3D
approaches was achieved when labelling vertebrae on 2D MIPs. The performance
was further improved using the proposed adversarial training regime that
effectively enforced local spine a priori knowledge during training. Lastly,
spine-localization increased the generalizability of our approach by
homogenizing the content in the MIPs.Comment: Published in Radiology:A
Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns
In medical imaging, the heterogeneity of multi-centre data impedes the
applicability of deep learning-based methods and results in significant
performance degradation when applying models in an unseen data domain, e.g. a
new centreor a new scanner. In this paper, we propose an unsupervised domain
adaptation framework for boosting image segmentation performance across
multiple domains without using any manual annotations from the new target
domains, but by re-calibrating the networks on few images from the target
domain. To achieve this, we enforce architectures to be adaptive to new data by
rejecting improbable segmentation patterns and implicitly learning through
semantic and boundary information, thus to capture disease-specific spatial
patterns in an adversarial optimization. The adaptation process needs
continuous monitoring, however, as we cannot assume the presence of
ground-truth masks for the target domain, we propose two new metrics to monitor
the adaptation process, and strategies to train the segmentation algorithm in a
stable fashion. We build upon well-established 2D and 3D architectures and
perform extensive experiments on three cross-centre brain lesion segmentation
tasks, involving multicentre public and in-house datasets. We demonstrate that
recalibrating the deep networks on a few unlabeled images from the target
domain improves the segmentation accuracy significantly.Comment: submitted to a journal and under revie
DiamondGAN: Unified Multi-Modal Generative Adversarial Networks for MRI Sequences Synthesis
Synthesizing MR imaging sequences is highly relevant in clinical practice, as
single sequences are often missing or are of poor quality (e.g. due to motion).
Naturally, the idea arises that a target modality would benefit from
multi-modal input, as proprietary information of individual modalities can be
synergistic. However, existing methods fail to scale up to multiple non-aligned
imaging modalities, facing common drawbacks of complex imaging sequences. We
propose a novel, scalable and multi-modal approach called DiamondGAN. Our model
is capable of performing exible non-aligned cross-modality synthesis and data
infill, when given multiple modalities or any of their arbitrary subsets,
learning structured information in an end-to-end fashion. We synthesize two MRI
sequences with clinical relevance (i.e., double inversion recovery (DIR) and
contrast-enhanced T1 (T1-c)), reconstructed from three common sequences. In
addition, we perform a multi-rater visual evaluation experiment and find that
trained radiologists are unable to distinguish synthetic DIR images from real
ones.Comment: accepted by miccai 201
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
We propose an auto-encoding network architecture for point clouds (PC)
capable of extracting shape signatures without supervision. Building on this,
we (i) design a loss function capable of modelling data variance on PCs which
are unstructured, and (ii) regularise the latent space as in a variational
auto-encoder, both of which increase the auto-encoders' descriptive capacity
while making them probabilistic. Evaluating the reconstruction quality of our
architectures, we employ them for detecting vertebral fractures without any
supervision. By learning to efficiently reconstruct only healthy vertebrae,
fractures are detected as anomalous reconstructions. Evaluating on a dataset
containing 1500 vertebrae, we achieve area-under-ROC curve of 75%,
without using intensity-based features.Comment: Accepted at Medical Image Computing and Computer-Assisted
Intervention (MICCAI), 2019; JSK and BHM are joint supervising author
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Robust localisation and identification of vertebrae is essential for
automated spine analysis. The contribution of this work to the task is
two-fold: (1) Inspired by the human expert, we hypothesise that a sagittal and
coronal reformation of the spine contain sufficient information for labelling
the vertebrae. Thereby, we propose a butterfly-shaped network architecture
(termed Btrfly Net) that efficiently combines the information across
reformations. (2) Underpinning the Btrfly net, we present an energy-based
adversarial training regime that encodes local spine structure as an anatomical
prior into the network, thereby enabling it to achieve state-of-art performance
in all standard metrics on a benchmark dataset of 302 scans without any
post-processing during inference.Comment: Published as conference paper in Medical Image Computing and Computer
Assisted Intervention - MICCAI 201
Red-GAN: Attacking class imbalance via conditioned generation. Yet another perspective on medical image synthesis for skin lesion dermoscopy and brain tumor MRI
Exploiting learning algorithms under scarce data regimes is a limitation and
a reality of the medical imaging field. In an attempt to mitigate the problem,
we propose a data augmentation protocol based on generative adversarial
networks. We condition the networks at a pixel-level (segmentation mask) and at
a global-level information (acquisition environment or lesion type). Such
conditioning provides immediate access to the image-label pairs while
controlling global class specific appearance of the synthesized images. To
stimulate synthesis of the features relevant for the segmentation task, an
additional passive player in a form of segmentor is introduced into the
adversarial game. We validate the approach on two medical datasets: BraTS,
ISIC. By controlling the class distribution through injection of synthetic
images into the training set we achieve control over the accuracy levels of the
datasets' classes