Automatic cerebral hemisphere segmentation in rat MRI with lesions via attention-based convolutional neural networks

We present MedicDeepLabv3+, a convolutional neural network that is the first completely automatic method to segment cerebral hemispheres in magnetic resonance (MR) volumes of rats with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-field correction or registration to a template, produces segmentations in less than a second, and its GPU memory requirements can be adjusted based on the available resources. We optimized MedicDeepLabv3+ and six other state-of-the-art convolutional neural networks (DeepLabv3+, UNet, HighRes3DNet, V-Net, VoxResNet, Demon) on a heterogeneous training set comprised by MR volumes from 11 cohorts acquired at different lesion stages. Then, we evaluated the trained models and two approaches specifically designed for rodent MRI skull stripping (RATS and RBET) on a large dataset of 655 MR rat brain volumes. In our experiments, MedicDeepLabv3+ outperformed the other methods, yielding an average Dice coefficient of 0.952 and 0.944 in the brain and contralateral hemisphere regions. Additionally, we show that despite limiting the GPU memory and the training data, our MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus, yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rat neuroimaging studies.Comment: Published in NeuroInformatic

RatLesNetv2: a fully convolutional network for rodent brain lesion segmentation

We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2

Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

Brain semantic segmentation: a deep learning approach in human and Rat MRI studies

Dissertação de mestrado em Biomedical Engineering Dissertation (área de especialização em Field of Medical informatics)Magnetic Resonance Imaging (MRI) provides information about anatomy and pathology. This type of technique is the most popular used for the study of rat and human brain. Classifying voxels according to the presence of relevant anatomic features is an important step in the pre-processing of the data. A precise delineation and automatic segmentation of the brain structures is required in preclinical rodent imaging field and can substitute the manual segmentation where time consuming or human-error problems can occur. Current solutions are based on traditional segmentation algorithms that raise accuracy issues and generally need human intervention during or after the segmentation process. In the humans’ field, most of the tools created in DL (deep learning) are used in tumour or lesion segmentation. Brain segmentation tissues are not as explored as oncology problems and lesions complications. In the rats’ field, there are no segmentation studies in DL. It was decided to use a DL approach in Rats to solve some of the old techniques’ problems. This dissertation will present an approach on semantic segmentation of white matter and gray matter in Human’s images, evaluate the algorithm’s performance with outliers. It will also present an FCN (fully convolutional network) solution for on semantic segmentation using rat’s and human’s MRI of anatomical features. A two-dimensional convolution (slice-by-slice) approach and a three-dimensional (volume) convolutions approach were evaluated. At the end, the results found, using FCN U-NET in rats’ MRI for a 2D convolutions approach, DSC were 94.65 % for WM, 91.03% for GM and 76.89 % for cerebrospinal fluid. Using the 3D convolutions approach, the results using DSC found are 93.81 % for WM, 89.69 % for GM and 74.68 % for cerebrospinal fluid. The results using humans’ MRI using DSC were 91.59% for WM and 84,58% for GM.Imagens de ressonância magnética providenciam informação acerca da anatomia humana e possíveis patologias existentes. Este é o tipo de técnica mais popular entre os estudos na área da neurociência, tanto em humanos como em roedores. A classificação de voxéis de acordo com a presença de informação anatómica relevante é um importante passo no pré-processamento de dados na comunidade científica na área da neurociência. Uma delineação precisa das várias estruturas do cérebro humano ou roedor é uma das requisições para a maioria dos estudos clínicos de imagens de ressonância magnética. A segmentação automática através de inteligência artificial pode vir a substituir ferramentas ou algoritmos semiautomáticos já existentes ou substituir também a segmentação manual que se trata de um processo muito demorado que está ligado a erro-humano. O avanço tecnológico provocou um estudo mais aprofundado no Deep Learning (DL) a partir de 2012, provando que estas técnicas de inteligência artificial estão a revelar-se melhores do que o que já existe na área médica. Dos estudos com ressonância magnética em humanos, a maioria das ferramentas criadas que utilizam DL são usadas na segmentação de tumores ou lesões cerebrais. A segmentação de tecidos cerebrais não está tão explorada como problemas oncológicos ou lesões cerebrais. Dos estudos com ressonância magnética em roedores, não existem ferramentas que utilizam as técnicas de DL. Tendo em conta que as técnicas de segmentação que já existem ainda têm muitas complicações e erros, foi decidido tentar uma abordagem de DL em Roedores, também. Esta dissertação irá apresentar uma abordagem de segmentação semântica de massa branca e massa cinzenta utilizando técnicas de DL em humanos. Irá também verificar a capacidade de generalização com casos de pacientes idosos. Irá ser apresentado uma técnica de DL nas imagens de ressonância magnética em roedores para a segmentação semântica de massa branca, massa cinzenta e líquido cérebroespinal. No final irá ser comparado as técnicas entre as duas espécies e também entre a utilização de convoluções com duas dimensões e de convoluções com três dimensões nos roedores. No final, os resultados encontrados utilizando uma FCN em Ratazanas numa abordagem 2D, os valores de DSC foram 94,65 % para massa branca, 91.03% para massa cinzenta e 76.89 % para o líquido cérebroespinal. Na abordagem 3D ,os valores de DSC encontrados foram 93.81 % % para massa branca, 89.69 % para massa cinzenta e 74.68 % para o líquido cérebroespinal. Os resultados utilizando as imagens humanas, foram 91.59% para massa branca e 84,58% para massa cinzenta.This work is part of the SIGMA project with the reference FCT-ANR/NEU-OSD/0258/2012, co-financed by the French public funding agency ANR (Agence Nationale pour la Recherche, APP Blanc International II 2012), the Portuguese FCT (Fundação para a Ciência e Tecnologia) and the Portuguese North Regional Operational Program (ON.2 – O Novo Norte) under the National Strategic Reference Framework (QREN), through the European Regional Development Fund (FEDER) as well as the Projecto Estratégico co-funded by FCT (PEst-C/SAU/LA0026-/2013) and the European Regional Development Fund COMPETE (FCOMP-01-0124-FEDER-037298).This work was also supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT within the Project Scope: UID/CEC/00319/2013

High-resolution micro-CT for 3D infarct characterization and segmentation in mice stroke models

Characterization of brain infarct lesions in rodent models of stroke is crucial to assess stroke pathophysiology and therapy outcome. Until recently, the analysis of brain lesions was performed using two techniques: (1) histological methods, such as TTC (Triphenyltetrazolium chloride), a time-consuming and inaccurate process; or (2) MRI imaging, a faster, 3D imaging method, that comes at a high cost. In the last decade, high-resolution micro-CT for 3D sample analysis turned into a simple, fast, and cheaper solution. Here, we successfully describe the application of brain contrasting agents (Osmium tetroxide and inorganic iodine) for high-resolution micro-CT imaging for fine location and quantification of ischemic lesion and edema in mouse preclinical stroke models. We used the intraluminal transient MCAO (Middle Cerebral Artery Occlusion) mouse stroke model to identify and quantify ischemic lesion and edema, and segment core and penumbra regions at different time points after ischemia, by manual and automatic methods. In the transient-ischemic-attack (TIA) mouse model, we can quantify striatal myelinated fibers degeneration. Of note, whole brain 3D reconstructions allow brain atlas co-registration, to identify the affected brain areas, and correlate them with functional impairment. This methodology proves to be a breakthrough in the field, by providing a precise and detailed assessment of stroke outcomes in preclinical animal studies

Methods for the analysis and characterization of brain morphology from MRI images

Brain magnetic resonance imaging (MRI) is an imaging modality that produces detailed images of the brain without using any ionizing radiation. From a structural MRI scan, it is possible to extract morphological properties of different brain regions, such as their volume and shape. These measures can both allow a better understanding of how the brain changes due to multiple factors (e.g., environmental and pathological) and contribute to the identification of new imaging biomarkers of neurological and psychiatric diseases. The overall goal of the present thesis is to advance the knowledge on how brain MRI image processing can be effectively used to analyze and characterize brain structure. The first two works presented in this thesis are animal studies that primarily aim to use MRI data for analyzing differences between groups of interest. In Paper I, MRI scans from wild and domestic rabbits were processed to identify structural brain differences between these two groups. Domestication was found to significantly reshape brain structure in terms of both regional gray matter volume and white matter integrity. In Paper II, rat brain MRI scans were used to train a brain age prediction model. This model was then tested on both controls and a group of rats that underwent long-term environmental enrichment and dietary restriction. This healthy lifestyle intervention was shown to significantly affect the predicted brain age trajectories by slowing the rats’ aging process compared to controls. Furthermore, brain age predicted on young adult rats was found to have a significant effect on survival. Papers III to V are human studies that propose deep learning-based methods for segmenting brain structures that can be severely affected by neurodegeneration. In particular, Papers III and IV focus on U-Net-based 2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS) patients. In both studies, good segmentation accuracy was obtained and a significant correlation was found between CC area and the patient’s level of cognitive and physical disability. Additionally, in Paper IV, shape analysis of the segmented CC revealed a significant association between disability and both CC thickness and bending angle. Conversely, in Paper V, a novel method for automatic segmentation of the hippocampus is proposed, which consists of embedding a statistical shape prior as context information into a U-Net-based framework. The inclusion of shape information was shown to significantly improve segmentation accuracy when testing the method on a new unseen cohort (i.e., different from the one used for training). Furthermore, good performance was observed across three different diagnostic groups (healthy controls, subjects with mild cognitive impairment and Alzheimer’s patients) that were characterized by different levels of hippocampal atrophy. In summary, the studies presented in this thesis support the great value of MRI image analysis for the advancement of neuroscientific knowledge, and their contribution is mostly two-fold. First, by applying well-established processing methods on datasets that had not yet been explored in the literature, it was possible to characterize specific brain changes and disentangle relevant problems of a clinical or biological nature. Second, a technical contribution is provided by modifying and extending already-existing brain image processing methods to achieve good performance on new datasets

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net

A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care