898 research outputs found
Context encoding enables machine learning-based quantitative photoacoustics
Real-time monitoring of functional tissue parameters, such as local blood
oxygenation, based on optical imaging could provide groundbreaking advances in
the diagnosis and interventional therapy of various diseases. While
photoacoustic (PA) imaging is a novel modality with great potential to measure
optical absorption deep inside tissue, quantification of the measurements
remains a major challenge. In this paper, we introduce the first machine
learning based approach to quantitative PA imaging (qPAI), which relies on
learning the fluence in a voxel to deduce the corresponding optical absorption.
The method encodes relevant information of the measured signal and the
characteristics of the imaging system in voxel-based feature vectors, which
allow the generation of thousands of training samples from a single simulated
PA image. Comprehensive in silico experiments suggest that context encoding
(CE)-qPAI enables highly accurate and robust quantification of the local
fluence and thereby the optical absorption from PA images.Comment: under review JB
Hyperspectral Camera Selection for Interventional Health-care
Hyperspectral imaging (HSI) is an emerging modality in health-care
applications for disease diagnosis, tissue assessment and image-guided surgery.
Tissue reflectances captured by a HSI camera encode physiological properties
including oxygenation and blood volume fraction. Optimal camera properties such
as filter responses depend crucially on the application, and choosing a
suitable HSI camera for a research project and/or a clinical problem is not
straightforward. We propose a generic framework for quantitative and
application-specific performance assessment of HSI cameras and optical
subsystem without the need for any physical setup. Based on user input about
the camera characteristics and properties of the target domain, our framework
quantifies the performance of the given camera configuration using large
amounts of simulated data and a user-defined metric. The application of the
framework to commercial camera selection and band selection in the context of
oxygenation monitoring in interventional health-care demonstrates its
integration into the design work-flow of an engineer. The advantage of being
able to test the desired configuration without the need for purchasing
expensive components may save system engineers valuable resources
Tract orientation mapping for bundle-specific tractography
While the major white matter tracts are of great interest to numerous studies
in neuroscience and medicine, their manual dissection in larger cohorts from
diffusion MRI tractograms is time-consuming, requires expert knowledge and is
hard to reproduce. Tract orientation mapping (TOM) is a novel concept that
facilitates bundle-specific tractography based on a learned mapping from the
original fiber orientation distribution function (fODF) peaks to a list of
tract orientation maps (also abbr. TOM). Each TOM represents one of the known
tracts with each voxel containing no more than one orientation vector. TOMs can
act as a prior or even as direct input for tractography. We use an
encoder-decoder fully-convolutional neural network architecture to learn the
required mapping. In comparison to previous concepts for the reconstruction of
specific bundles, the presented one avoids various cumbersome processing steps
like whole brain tractography, atlas registration or clustering. We compare it
to four state of the art bundle recognition methods on 20 different bundles in
a total of 105 subjects from the Human Connectome Project. Results are
anatomically convincing even for difficult tracts, while reaching low angular
errors, unprecedented runtimes and top accuracy values (Dice). Our code and our
data are openly available.Comment: Accepted at MICCAI 201
Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)
One of the main applications of photoacoustic (PA) imaging is the recovery of
functional tissue properties, such as blood oxygenation (sO2). This is
typically achieved by linear spectral unmixing of relevant chromophores from
multispectral photoacoustic images. Despite the progress that has been made
towards quantitative PA imaging (qPAI), most sO2 estimation methods yield poor
results in realistic settings. In this work, we tackle the challenge by
employing learned spectral decoloring for quantitative photoacoustic imaging
(LSD-qPAI) to obtain quantitative estimates for blood oxygenation. LSD-qPAI
computes sO2 directly from pixel-wise initial pressure spectra Sp0, which are
vectors comprised of the initial pressure at the same spatial location over all
recorded wavelengths. Initial results suggest that LSD-qPAI is able to obtain
accurate sO2 estimates directly from multispectral photoacoustic measurements
in silico and plausible estimates in vivo.Comment: 5 page
Towards whole-body CT Bone Segmentation
Bone segmentation from CT images is a task that has been worked on for
decades. It is an important ingredient to several diagnostics or treatment
planning approaches and relevant to various diseases. As high-quality manual
and semi-automatic bone segmentation is very time-consuming, a reliable and
fully automatic approach would be of great interest in many scenarios. In this
publication, we propose a UNet inspired architecture to address the task using
Deep Learning. We evaluated the approach on whole-body CT scans of patients
suffering from multiple myeloma. As the disease decomposes the bone, an
accurate segmentation is of utmost importance for the evaluation of bone
density, disease staging and localization of focal lesions. The method was
evaluated on an in-house data-set of 6000 2D image slices taken from 15
whole-body CT scans, achieving a dice score of 0.96 and an IOU of 0.94.Comment: Accepted conference paper at BVM 201
Direct White Matter Bundle Segmentation using Stacked U-Nets
The state-of-the-art method for automatically segmenting white matter bundles
in diffusion-weighted MRI is tractography in conjunction with streamline
cluster selection. This process involves long chains of processing steps which
are not only computationally expensive but also complex to setup and tedious
with respect to quality control. Direct bundle segmentation methods treat the
task as a traditional image segmentation problem. While they so far did not
deliver competitive results, they can potentially mitigate many of the
mentioned issues. We present a novel supervised approach for direct tract
segmentation that shows major performance gains. It builds upon a stacked U-Net
architecture which is trained on manual bundle segmentations from Human
Connectome Project subjects. We evaluate our approach \textit{in vivo} as well
as \textit{in silico} using the ISMRM 2015 Tractography Challenge phantom
dataset. We achieve human segmentation performance and a major performance gain
over previous pipelines. We show how the learned spatial priors efficiently
guide the segmentation even at lower image qualities with little quality loss
Task Fingerprinting for Meta Learning in Biomedical Image Analysis
Shortage of annotated data is one of the greatest bottlenecks in biomedical
image analysis. Meta learning studies how learning systems can increase in
efficiency through experience and could thus evolve as an important concept to
overcome data sparsity. However, the core capability of meta learning-based
approaches is the identification of similar previous tasks given a new task - a
challenge largely unexplored in the biomedical imaging domain. In this paper,
we address the problem of quantifying task similarity with a concept that we
refer to as task fingerprinting. The concept involves converting a given task,
represented by imaging data and corresponding labels, to a fixed-length vector
representation. In fingerprint space, different tasks can be directly compared
irrespective of their data set sizes, types of labels or specific resolutions.
An initial feasibility study in the field of surgical data science (SDS) with
26 classification tasks from various medical and non-medical domains suggests
that task fingerprinting could be leveraged for both (1) selecting appropriate
data sets for pretraining and (2) selecting appropriate architectures for a new
task. Task fingerprinting could thus become an important tool for meta learning
in SDS and other fields of biomedical image analysis.Comment: Medical Image Computing and Computer Assisted Interventions (MICCAI)
202
No New-Net
In this paper we demonstrate the effectiveness of a well trained U-Net in the
context of the BraTS 2018 challenge. This endeavour is particularly interesting
given that researchers are currently besting each other with architectural
modifications that are intended to improve the segmentation performance. We
instead focus on the training process arguing that a well trained U-Net is hard
to beat. Our baseline U-Net, which has only minor modifications and is trained
with a large patch size and a Dice loss function indeed achieved competitive
Dice scores on the BraTS2018 validation data. By incorporating additional
measures such as region based training, additional training data, a simple
postprocessing technique and a combination of loss functions, we obtain Dice
scores of 77.88, 87.81 and 80.62, and Hausdorff Distances (95th percentile) of
2.90, 6.03 and 5.08 for the enhancing tumor, whole tumor and tumor core,
respectively on the test data. This setup achieved rank two in BraTS2018, with
more than 60 teams participating in the challenge
Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge
Quantitative analysis of brain tumors is critical for clinical decision
making. While manual segmentation is tedious, time consuming and subjective,
this task is at the same time very challenging to solve for automatic
segmentation methods. In this paper we present our most recent effort on
developing a robust segmentation algorithm in the form of a convolutional
neural network. Our network architecture was inspired by the popular U-Net and
has been carefully modified to maximize brain tumor segmentation performance.
We use a dice loss function to cope with class imbalances and use extensive
data augmentation to successfully prevent overfitting. Our method beats the
current state of the art on BraTS 2015, is one of the leading methods on the
BraTS 2017 validation set (dice scores of 0.896, 0.797 and 0.732 for whole
tumor, tumor core and enhancing tumor, respectively) and achieves very good
Dice scores on the test set (0.858 for whole, 0.775 for core and 0.647 for
enhancing tumor). We furthermore take part in the survival prediction
subchallenge by training an ensemble of a random forest regressor and
multilayer perceptrons on shape features describing the tumor subregions. Our
approach achieves 52.6% accuracy, a Spearman correlation coefficient of 0.496
and a mean square error of 209607 on the test set
OR-UNet: an Optimized Robust Residual U-Net for Instrument Segmentation in Endoscopic Images
Segmentation of endoscopic images is an essential processing step for
computer and robotics-assisted interventions. The Robust-MIS challenge provides
the largest dataset of annotated endoscopic images to date, with 5983 manually
annotated images. Here we describe OR-UNet, our optimized robust residual 2D
U-Net for endoscopic image segmentation. As the name implies, the network makes
use of residual connections in the encoder. It is trained with the sum of Dice
and cross-entropy loss and deep supervision. During training, extensive data
augmentation is used to increase the robustness. In an 8-fold cross-validation
on the training images, our model achieved a mean (median) Dice score of 87.41
(94.35). We use the eight models from the cross-validation as an ensemble on
the test set
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