Scalable multimodal convolutional networks for brain tumour segmentation

Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimodal deep learning architecture using new nested structures that explicitly leverage deep features within or across modalities. This aims at making the early layers of the architecture structured and sparse so that the final architecture becomes scalable to the number of modalities. We evaluate the scalable architecture for brain tumour segmentation and give evidence of its regularisation effect compared to the conventional concatenation approach.Comment: Paper accepted at MICCAI 201

Learning joint lesion and tissue segmentation from task-specific hetero-modal datasets

Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving performance levels making it of interest for clinical use. However, few existing approaches allow for jointly segmenting normal tissue and brain lesions. Developing a DNN for such joint task is currently hampered by the fact that annotated datasets typically address only one specific task and rely on a task-specific hetero-modal imaging protocol. In this work, we propose a novel approach to build a joint tissue and lesion segmentation model from task-specific hetero-modal and partially annotated datasets. Starting from a variational formulation of the joint problem, we show how the expected risk can be decomposed and optimised empirically. We exploit an upper-bound of the risk to deal with missing imaging modalities. For each task, our approach reaches comparable performance than task-specific and fully-supervised models.Comment: Accepted as an oral presentation at MIDL 2019 [arXiv:1907.08612

Volitional Control of Brain Motor Activity and Its Therapeutic Potential

BACKGROUND: Neurofeedback training is a closed-loop neuromodulatory technique in which real-time feedback of brain activity and connectivity is provided to the participant for the purpose of volitional neural control. Through practice and reinforcement, such learning has been shown to facilitate measurable changes in brain function and behavior. OBJECTIVES: In this review, we examine how neurofeedback, coupled with motor imagery training, has the potential to improve or normalize motor function in neurological diseases such as Parkinson disease and chronic stroke. We will also explore neurofeedback in the context of brain-machine interfaces (BMIs), discussing both noninvasive and invasive methods which have been used to power external devices (eg, robot hand orthosis or exoskeleton) in the context of motor neurorehabilitation. CONCLUSIONS: The published literature provides mounting high-quality evidence that neurofeedback and BMI control may lead to clinically relevant changes in brain function and behavior. CLINICAL TRIAL REGISTRATION: The ClinicalTrials.gov registration number for the study is NCT00912041

The conversational position in endoscopic pituitary surgery

We describe a novel patient position for endoscopic transphenoidal surgery - the 'conversational position'. This position is a safe and effective alternative to the standard supine position, incorporating a semi-sitting position with the additional innovation of achieving a 'conversational position' by flexing the neck and turning the patient's head turned to face the surgeon. The 'conversational' position offers improvements in the surgical approach to sellar region, addressing specific intraoperative challenges such as maintaining a bloodless operative field, and enabling more intuitive and ergonomic surgical workflow

Generalised wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks

The Dice score is widely used for binary segmentation due to its robustness to class imbalance. Soft generalisations of the Dice score allow it to be used as a loss function for training convolutional neural networks (CNN). Although CNNs trained using mean-class Dice score achieve state-of-the-art results on multi-class segmentation, this loss function does neither take advantage of inter-class relationships nor multi-scale information. We argue that an improved loss function should balance misclassifications to favour predictions that are semantically meaningful. This paper investigates these issues in the context of multi-class brain tumour segmentation. Our contribution is threefold. 1) We propose a semantically-informed generalisation of the Dice score for multi-class segmentation based on the Wasserstein distance on the probabilistic label space. 2) We propose a holistic CNN that embeds spatial information at multiple scales with deep supervision. 3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice scores achieves segmentations that are more semantically meaningful for brain tumour segmentation.Comment: Accepted as an oral presentation at the MICCAI 2017 Brain Lesion (BrainLes) Worksho

Volitional modulation of higher-order visual cortex alters human perception

Can we change our perception by controlling our brain activation? Awareness during binocular rivalry is shaped by the alternating perception of different stimuli presented separately to each monocular view. We tested the possibility of causally influencing the likelihood of a stimulus entering awareness. To do this, participants were trained with neurofeedback, using realtime functional magnetic resonance imaging (rt-fMRI), to differentially modulate activation in stimulus-selective visual cortex representing each of the monocular images. Neurofeedback training led to altered bistable perception associated with activity changes in the trained regions. The degree to which training influenced perception predicted changes in grey and white matter volumes of these regions. Short-term intensive neurofeedback training therefore sculpted the dynamics of visual awareness, with associated plasticity in the human brain