Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step

Aprendizado profundo para análise do cérebro em imagens de ressonância magnética

Orientadores: Roberto de Alencar Lotufo, Sebastien Ourselin e Leticia RittnerDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação e University College LondonResumo: Redes neurais convolucionais (CNNs-Convolutional neural networks) são uma vertente do apredizado profundo que obtiveram muito sucesso quando aplicadas em várias análises em imagens de ressonância magnética (MR-magnetic resonance) do cérebro. As CNNs são métodos de aprendizagem de representação com várias camadas empilhadas compostas por uma operação de convolução seguida de uma ativação não linear e de camadas de agru- pamento. Nessas redes, cada camada gera uma representação mais alta e mais abstrata de uma determinada entrada, na qual os pesos das camadas convolucionais são aprendidos por um problema de otimização. Neste trabalho, tratamos dois problemas usando aborda- gens baseadas em aprendizagem profunda: remoção da calota craniana (SS) e tractografia. Primeiramente, propusemos um SS completo baseado em CNN treinado com o que nos referimos como máscaras de padrão de prata. A segmentação de tecido cerebral a partir de tecido não cerebral é um processo conhecido como extração da calota craniana ou re- moção de crânios. As máscaras de padrão de prata são geradas pela formação do consenso a partir de um conjunto de oito métodos de SS públicos, não baseados em aprendizagem profunda, usando o algoritmo Verdade Simultânea e Estimativa do Nível de Desempenho (STAPLE-Simultaneous Truth and Performance Level Estimation). Nossa abordagem al- cançou o desempenho do estado da arte, generalizou de forma otimizada, diminuiu a variabilidade inter / intra-avaliador e evitou a super-especialização da segmentação da CNN em relação a uma anotação manual específica. Em segundo lugar, investigamos uma solução de tractografia baseada em CNN para cirurgia de epilepsia. O principal objetivo desta análise foi estruturar uma linha de base para uma regressão baseada em aprendiza- gem profunda para prever as orientações da fibra da matéria branca. Tractografia é uma visualização das fibras ou tratos da substância branca; seu objetivo no planejamento pré- operatório é simplesmente identificar a posição de caminhos eloqüentes, como os tratos motor, sensorial e de linguagem, para reduzir o risco de danificar essas estruturas críticas. Realizamos uma análise em um único paciente e também uma análise entre 10 pacientes em uma abordagem de validação cruzada. Nossos resultados não foram ótimos, entretanto, as fibras preditas pelo algoritmo tenderam a ter um comprimento similar e convergiram para os locais médios do trato das fibras. Além disso, até onde sabemos, nosso método é a primeira abordagem que investiga CNNs para tractografia, e assim, nosso trabalho é uma base para este tópicoAbstract: Convolutional neural networks (CNNs) are one branch of deep learning that have per- formed successfully in many brain magnetic resonance (MR) imaging analysis. CNNs are representation-learning methods with stacked layers comprised of a convolution op- eration followed by a non-linear activation and pooling layers. In these networks, each layer outputs a higher and more abstract representation from a given input, in which the weights of the convolutional layers are learned by an optimization problem. In this work, we tackled two problems using deep-learning-based approaches: skull-stripping (SS) and tractography. We firstly proposed a full CNN-based SS trained with what we refer to as silver standard masks. Segmenting brain tissue from non-brain tissue is a process known as brain extraction or skull-stripping. Silver standard masks are generated by forming the consensus from a set of eight, public, non-deep-learning-based SS methods using the algo- rithm Simultaneous Truth and Performance Level Estimation (STAPLE). Our approach reached state-of-the-art performance, generalized optimally, decreased inter-/intra-rater variability, and avoided CNN segmentation overfitting towards one specific manual anno- tation. Secondly, we investigated a CNN-based tractography solution for epilepsy surgery. The main goal of this analysis was to structure a baseline for a deep-learning-based- regression to predict white matter fiber orientations. Tractography is a visualization of the white matter fibers or tracts; its goal in presurgical planing is simply to identify the position of eloquent pathways, such as the motor, sensory, and language tracts to reduce the risk of damaging these critical structures. We performed analysis cross-validation us- ing only in a single patient per time, and also, training with data from 10 patients for training the CNN. Our results were not optimal, however, the tracts tended to be of a similar length and converged to the mean fiber tract locations. Additionally, to the best of our knowledge, our method is the first approach that investigates CNNs for tractography, and thus, our work is a baseline for this topicMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica2016/18332-8, 2017/23747-5FAPES

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features

Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks

Manual annotation is considered to be the “gold standard” in medical imaging analysis. However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. Moreover, single-rater manual annotation is most often used in data-driven approaches making the network biased to only that single expert. In this work, we propose a CNN for brain extraction in magnetic resonance (MR) imaging, that is fully trained with what we refer to as “silver standard” masks. Therefore, eliminating the cost associated with manual annotation. Silver standard masks are generated by forming the consensus from a set of eight, public, non-deep-learning-based brain extraction methods using the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Our method consists of (1) developing a dataset with “silver standard” masks as input, and implementing (2) a tri-planar method using parallel 2D U-Net-based convolutional neural networks (CNNs) (referred to as CONSNet). This term refers to our integrated approach, i.e., training with silver standard masks and using a 2D U-Net-based architecture. We conducted our analysis using three public datasets: the Calgary-Campinas-359 (CC-359), the LONI Probabilistic Brain Atlas (LPBA40), and the Open Access Series of Imaging Studies (OASIS). Five performance metrics were used in our experiments: Dice coefficient, sensitivity, specificity, Hausdorff distance, and symmetric surface-to-surface mean distance. Our results showed that we outperformed (i.e., larger Dice coefficients) the current state-of-the-art skull-stripping methods without using gold standard annotation for the CNNs training stage. CONSNet is the first deep learning approach that is fully trained using silver standard data and is, thus, more generalizable. Using these masks, we eliminate the cost of manual annotation, decreased inter-/intra-rater variability, and avoided CNN segmentation overfitting towards one specific manual annotation guideline that can occur when gold standard masks are used. Moreover, once trained, our method takes few seconds to process a typical brain image volume using modern a high-end GPU. In contrast, many of the other competitive methods have processing times in the order of minutes984858CAPES - Coordenação de Aperfeiçoamento de Pessoal e Nível SuperiorCNPQ - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo88881.062158/2014-01308311/2016-7; 311228/2014-32013/07559-3; 2016/18332-8This project was supported by FAPESP CEPID-BRAINN (2013/07559-3) and CAPES PVE (88881.062158/2014-01). Oeslle Lucena thanks FAPESP (2016/18332-8), Roberto Souza thanks the Natural Science and Engineering Research Council of Canada Collaborative Research and Training Experience International and Industrial Imaging Training (NSERC CREATE I3T) Program and the Hotchkiss Brain Institute, Letícia Rittner thanks CNPq (308311/2016-7), Richard Frayne is supported by the NSERC (261754-2013), Canadian Institutes for Health Research (CIHR, MOP-333931) and the Hopewell Professorship in Brain Imaging, and Roberto Lotufo thanks CNPq (311228/2014-3

Enhancing the estimation of fiber orientation distributions using convolutional neural networks

Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and High-Resolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments

Synthetic Sleep EEG Signal Generation using Latent Diffusion Models

International audienceElectroencephalography (EEG) is a non-invasive method that allows for recording rich temporal information and is a valuable tool for diagnosing various neurological and psychiatric conditions. One of the main limitations of EEG is the low signal-to-noise ratio and the lack of data availability to train large data-hungry neural networks. Sharing large healthcare datasets is crucial to advancing medical imaging research, but privacy concerns often impede such efforts. Deep generative models have gained attention as a way to circumvent data-sharing limitations and as a possible way to generate data to improve the performance of these models. This work investigates latent diffusion models with spectral loss as deep generative modeling to generate 30-second windows of synthetic EEG signals of sleep stages. The spectral loss is essential to guarantee that the generated signal contains structured oscillations on specific frequency bands that are typical of EEG signals. We trained our models using two large sleep datasets (Sleep EDFx and SHHS) and used the Multi-Scale Structural Similarity Metric, Frechet inception distance, and a spectrogram analysis to evaluate the quality of synthetic signals. We demonstrate that the latent diffusion model can generate realistic signals with the correct neural oscillation and could, therefore, be used to overcome the scarcity of EEG data