Sparse Projections of Medical Images onto Manifolds

Manifold learning has been successfully applied to a variety of medical imaging problems. Its use in real-time applications requires fast projection onto the low-dimensional space. To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold. Commonly used approaches such as the Nyström extension and kernel ridge regression require using all training points. We propose an interpolation function that only depends on a small subset of the input training data. Consequently, in the testing phase each new point only needs to be compared against a small number of input training data in order to project the point onto the low-dimensional space. We interpret our method as an out-of-sample extension that approximates kernel ridge regression. Our method involves solving a simple convex optimization problem and has the attractive property of guaranteeing an upper bound on the approximation error, which is crucial for medical applications. Tuning this error bound controls the sparsity of the resulting interpolation function. We illustrate our method in two clinical applications that require fast mapping of input images onto a low-dimensional space.National Alliance for Medical Image Computing (U.S.) (grant NIH NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (grant NIH NCRR NAC P41-RR13218)National Institutes of Health (U.S.) (grant NIH NIBIB NAC P41-EB-015902

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding

Groupwise Multimodal Image Registration using Joint Total Variation

In medical imaging it is common practice to acquire a wide range of modalities (MRI, CT, PET, etc.), to highlight different structures or pathologies. As patient movement between scans or scanning session is unavoidable, registration is often an essential step before any subsequent image analysis. In this paper, we introduce a cost function based on joint total variation for such multimodal image registration. This cost function has the advantage of enabling principled, groupwise alignment of multiple images, whilst being insensitive to strong intensity non-uniformities. We evaluate our algorithm on rigidly aligning both simulated and real 3D brain scans. This validation shows robustness to strong intensity non-uniformities and low registration errors for CT/PET to MRI alignment. Our implementation is publicly available at https://github.com/brudfors/coregistration-njtv

Image-level harmonization of multi-site data using image-and-spatial transformer networks

We investigate the use of image-and-spatial transformer networks (ISTNs) to tackle domain shift in multi-site medical imaging data. Commonly, domain adaptation (DA) is performed with little regard for explainability of the inter-domain transformation and is often conducted at the feature-level in the latent space. We employ ISTNs for DA at the image-level which constrains transformations to explainable appearance and shape changes. As proof-of-concept we demonstrate that ISTNs can be trained adversarially on a classification problem with simulated 2D data. For real-data validation, we construct two 3D brain MRI datasets from the Cam-CAN and UK Biobank studies to investigate domain shift due to acquisition and population differences. We show that age regression and sex classification models trained on ISTN output improve generalization when training on data from one and testing on the other site

Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks

Both image registration and label fusion in the multi-atlas segmentation (MAS) rely on the intensity similarity between target and atlas images. However, such similarity can be problematic when target and atlas images are acquired using different imaging protocols. High-level structure information can provide reliable similarity measurement for cross-modality images when cooperating with deep neural networks (DNNs). This work presents a new MAS framework for cross-modality images, where both image registration and label fusion are achieved by DNNs. For image registration, we propose a consistent registration network, which can jointly estimate forward and backward dense displacement fields (DDFs). Additionally, an invertible constraint is employed in the network to reduce the correspondence ambiguity of the estimated DDFs. For label fusion, we adapt a few-shot learning network to measure the similarity of atlas and target patches. Moreover, the network can be seamlessly integrated into the patch-based label fusion. The proposed framework is evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework is effective in both cross-modality registration and segmentation

Pulse Sequence Resilient Fast Brain Segmentation

Accurate automatic segmentation of brain anatomy from $T_1$-weighted~($T_1$-w) magnetic resonance images~(MRI) has been a computationally intensive bottleneck in neuroimaging pipelines, with state-of-the-art results obtained by unsupervised intensity modeling-based methods and multi-atlas registration and label fusion. With the advent of powerful supervised convolutional neural networks~(CNN)-based learning algorithms, it is now possible to produce a high quality brain segmentation within seconds. However, the very supervised nature of these methods makes it difficult to generalize them on data different from what they have been trained on. Modern neuroimaging studies are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is not possible to standardize the whole gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input $T_1$-w acquisition. Our approach relies on building approximate forward models of $T_1$-w pulse sequences that produce a typical test image. We use the forward models to augment the training data with test data specific training examples. These augmented data can be used to update and/or build a more robust segmentation model that is more attuned to the test data imaging properties. Our method generates highly accurate, state-of-the-art segmentation results~(overall Dice overlap=0.94), within seconds and is consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201

Predicting Activation Across Individuals with Resting-State Functional Connectivity Based Multi-Atlas Label Fusion

The alignment of brain imaging data for functional neuroimaging studies is challenging due to the discrepancy between correspondence of morphology, and equivalence of functional role. In this paper we map functional activation areas across individuals by a multi-atlas label fusion algorithm in a functional space. We learn the manifold of resting-state fMRI signals in each individual, and perform manifold alignment in an embedding space. We then transfer activation predictions from a source population to a target subject via multi-atlas label fusion. The cost function is derived from the aligned manifolds, so that the resulting correspondences are derived based on the similarity of intrinsic connectivity architecture. Experiments show that the resulting label fusion predicts activation evoked by various experiment conditions with higher accuracy than relying on morphological alignment. Interestingly, the distribution of this gain is distributed heterogeneously across the cortex, and across tasks. This offers insights into the relationship between intrinsic connectivity, morphology and task activation. Practically, the mechanism can serve as prior, and provides an avenue to infer task-related activation in individuals for whom only resting data is available. Keywords: Functional Connectivity, Cortical Surface, Task Activation, Target Subject, Intrinsic ConnectivityCongressionally Directed Medical Research Programs (U.S.) (Grant PT100120)Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) (R01HD067312)Neuroimaging Analysis Center (U.S.) (P41EB015902)Oesterreichische Nationalbank (14812)Oesterreichische Nationalbank (15929)Seventh Framework Programme (European Commission) (FP7 2012-PIEF-GA-33003

Towards Whole Placenta Segmentation At Late Gestation Using Multi-View Ultrasound Images

We propose a method to extract the human placenta at late gestation using multi-view 3D US images. This is the first step towards automatic quantification of placental volume and morphology from US images along the whole pregnancy beyond early stages (where the entire placenta can be captured with a single 3D US image). Our method uses 3D US images from different views acquired with a multi-probe system. A whole placenta segmentation is obtained from these images by using a novel technique based on 3D convolutional neural networks. We demonstrate the performance of our method on 3D US images of the placenta in the last trimester. We achieve a high Dice overlap of up to 0.8 with respect to manual annotations, and the derived placental volumes are comparable to corresponding volumes extracted from MR.Wellcome Trust IEH Award; EPSRC Centre for Medical Engineering; National Institute for Health Research (NIHR); King’s College London; NHS Foundation Trus

The effectiveness of e-Learning on biosecurity practice to slow the spread of invasive alien species

Online e-Learning is increasingly being used to provide environmental training. Prevention measures including biosecurity are essential to reducing the introduction and spread of invasive alien species (IAS) and are central to international and national IAS policy. This paper is the first to evaluate the effectiveness of e-Learning as a tool to increase awareness, risk perception and biosecurity behaviour in relation to IAS among individuals conducting work activities or research (fieldwork) in the field. We surveyed participants (a mixture of students and professionals) before, and 6 months after undertaking an e-Learning course on IAS and biosecurity practices. Awareness of IAS and self-reported biosecurity behaviour increased after e-Learning among students and professionals. Students had a lower awareness of IAS than professionals before training (20% of students vs 60% of professionals), but after training students showed a greater increase in awareness which led to similar levels of awareness post-training (81%). Prior to training, risk perception was also lower amongst students than professionals (33% of students and 59% of professionals were aware of the risk that their activities posed to the accidental spread of IAS). There was no change in risk perception amongst professionals after training, however training led to a doubling of risk perception in students. E-Learning also led to an increase in reported biosecurity behaviour and cleaning practices and there were higher levels of biosecurity cleaning amongst professionals. The higher awareness and better biosecurity amongst professionals is likely to reflect their familiarity with the issues of IAS and day-to-day activities in the field. Our results suggest that e-Learning is an effective tool to raise awareness and encourage behaviour change among field workers and researchers in an attempt to reduce the risk of accidental introduction and spread of IAS