242 research outputs found
A look ahead: PET/MR versus PET/CT
Introduction: Integration of positron emission tomography (PET) and magnetic resonance (MR) has become a topic of increasing interest to the imaging community over the past two years. Objectives: In this text, the authors attempt to distinguish facts from fiction concerning such integrated systems. Analysis of existing information of combined imaging on existing brain PET/MR systems and imaging experience with PET-computed tomography (CT) is reviewed. Various types of system integration of PET and MR are discussed with completely independent systems on one hand and completely integrated systems with the possibility of simultaneous data acquisition on the other hand. Furthermore, it is discussed, what simultaneous data acquisition with nuclear imaging systems combined with MR or CT really means, as technical simultaneity may not be relevant in light of the pharmacokinetics of the nuclear tracers used. Discussion: The authors conclude that combining PET/MR is an interesting research endeavor with uncertain outcome. They argue that, while completely simultaneous brain applications are of research interest immediately, clinical applications do not currently warrant the construction of fully integrated systems. Systems adjacent to each other, where imaging tables are linked with a patient "shuttle” thereby requiring only patient translation but no repositioning, may be a good start to assess the value of integrated PET/M
Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
The lack of fine-grained annotations hinders the deployment of automated
diagnosis systems, which require human-interpretable justification for their
decision process. In this paper, we address the problem of weakly supervised
identification and localization of abnormalities in chest radiographs. To that
end, we introduce a novel loss function for training convolutional neural
networks increasing the \emph{localization confidence} and assisting the
overall \emph{disease identification}. The loss leverages both image- and
patch-level predictions to generate auxiliary supervision. Rather than forming
strictly binary from the predictions as done in previous loss formulations, we
create targets in a more customized manner, which allows the loss to account
for possible misclassification. We show that the supervision provided within
the proposed learning scheme leads to better performance and more precise
predictions on prevalent datasets for multiple-instance learning as well as on
the NIH~ChestX-Ray14 benchmark for disease recognition than previously used
losses
Self-Guided Multiple Instance Learning for Weakly Supervised Thoracic DiseaseClassification and Localizationin Chest Radiographs
Due to the high complexity of medical images and the scarcity of trained personnel, most large-scale radiological datasets are lacking fine-grained annotations and are often only described on image-level. These shortcomings hinder the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs in a multiple-instance learning setting. To that end, we introduce a novel loss function for training convolutional neural networks increasing the localization confidence and assisting the overall disease identification. The loss leverages both image-and patch-level predictions to generate auxiliary supervision and enables specific training at patch-level. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner. This way, the loss accounts for possible misclassification of less certain instances. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH ChestX-Ray14 benchmark for disease recognition than previously used losses
'A net for everyone': fully personalized and unsupervised neural networks trained with longitudinal data from a single patient
With the rise in importance of personalized medicine, we trained personalized
neural networks to detect tumor progression in longitudinal datasets. The model
was evaluated on two datasets with a total of 64 scans from 32 patients
diagnosed with glioblastoma multiforme (GBM). Contrast-enhanced T1w sequences
of brain magnetic resonance imaging (MRI) images were used in this study. For
each patient, we trained their own neural network using just two images from
different timepoints. Our approach uses a Wasserstein-GAN (generative
adversarial network), an unsupervised network architecture, to map the
differences between the two images. Using this map, the change in tumor volume
can be evaluated. Due to the combination of data augmentation and the network
architecture, co-registration of the two images is not needed. Furthermore, we
do not rely on any additional training data, (manual) annotations or
pre-training neural networks. The model received an AUC-score of 0.87 for tumor
change. We also introduced a modified RANO criteria, for which an accuracy of
66% can be achieved. We show that using data from just one patient can be used
to train deep neural networks to monitor tumor change
Prediction of low-keV monochromatic images from polyenergetic CT scans for improved automatic detection of pulmonary embolism
Detector-based spectral computed tomography is a recent dual-energy CT (DECT)
technology that offers the possibility of obtaining spectral information. From
this spectral data, different types of images can be derived, amongst others
virtual monoenergetic (monoE) images. MonoE images potentially exhibit
decreased artifacts, improve contrast, and overall contain lower noise values,
making them ideal candidates for better delineation and thus improved
diagnostic accuracy of vascular abnormalities.
In this paper, we are training convolutional neural networks~(CNN) that can
emulate the generation of monoE images from conventional single energy CT
acquisitions. For this task, we investigate several commonly used
image-translation methods. We demonstrate that these methods while creating
visually similar outputs, lead to a poorer performance when used for automatic
classification of pulmonary embolism (PE). We expand on these methods through
the use of a multi-task optimization approach, under which the networks achieve
improved classification as well as generation results, as reflected by PSNR and
SSIM scores. Further, evaluating our proposed framework on a subset of the
RSNA-PE challenge data set shows that we are able to improve the Area under the
Receiver Operating Characteristic curve (AuROC) in comparison to a na\"ive
classification approach from 0.8142 to 0.8420.Comment: 4 pages, ISBI 202
Pre-examinations Improve Automated Metastases Detection on Cranial MRI
Materials and methods: First, a dual-time approach was assessed, for which
the CNN was provided sequences of the MRI that initially depicted new MM
(diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only
contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion
of also the native T1-weighted images, T2-weighted images, and FLAIR sequences
of both time points (CNNdual_all).Second, results were compared with the
corresponding single time approaches, in which the CNN was provided exclusively
the respective sequences of the diagnosis MRI.Casewise diagnostic performance
parameters were calculated from 5-fold cross-validation.
Results: In total, 94 cases with 494 MMs were included. Overall, the highest
diagnostic performance was achieved by inclusion of only the contrast-enhanced
T1-weighted images of the diagnosis and of a prediagnosis MRI (CNNdual_ce,
sensitivity = 73%, PPV = 25%, F1-score = 36%). Using exclusively
contrast-enhanced T1-weighted images as input resulted in significantly less
false-positives (FPs) compared with inclusion of further sequences beyond
contrast-enhanced T1-weighted images (FPs = 5/7 for CNNdual_ce/CNNdual_all, P <
1e-5). Comparison of contrast-enhanced dual and mono time approaches revealed
that exclusion of prediagnosis MRI significantly increased FPs (FPs = 5/10 for
CNNdual_ce/CNNce, P < 1e-9).Approaches with only native sequences were clearly
inferior to CNNs that were provided contrast-enhanced sequences.
Conclusions: Automated MM detection on contrast-enhanced T1-weighted images
performed with high sensitivity. Frequent FPs due to artifacts and vessels were
significantly reduced by additional inclusion of prediagnosis MRI, but not by
inclusion of further sequences beyond contrast-enhanced T1-weighted images.
Future studies might investigate different change detection architectures for
computer-aided detection
Revealing Hidden Potentials of the q-Space Signal in Breast Cancer
Mammography screening for early detection of breast lesions currently suffers
from high amounts of false positive findings, which result in unnecessary
invasive biopsies. Diffusion-weighted MR images (DWI) can help to reduce many
of these false-positive findings prior to biopsy. Current approaches estimate
tissue properties by means of quantitative parameters taken from generative,
biophysical models fit to the q-space encoded signal under certain assumptions
regarding noise and spatial homogeneity. This process is prone to fitting
instability and partial information loss due to model simplicity. We reveal
unexplored potentials of the signal by integrating all data processing
components into a convolutional neural network (CNN) architecture that is
designed to propagate clinical target information down to the raw input images.
This approach enables simultaneous and target-specific optimization of image
normalization, signal exploitation, global representation learning and
classification. Using a multicentric data set of 222 patients, we demonstrate
that our approach significantly improves clinical decision making with respect
to the current state of the art.Comment: Accepted conference paper at MICCAI 201
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