72 research outputs found

    Scalable joint segmentation and registration framework for infant brain images

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    The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study

    LESS: Label-efficient Multi-scale Learning for Cytological Whole Slide Image Screening

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    In computational pathology, multiple instance learning (MIL) is widely used to circumvent the computational impasse in giga-pixel whole slide image (WSI) analysis. It usually consists of two stages: patch-level feature extraction and slide-level aggregation. Recently, pretrained models or self-supervised learning have been used to extract patch features, but they suffer from low effectiveness or inefficiency due to overlooking the task-specific supervision provided by slide labels. Here we propose a weakly-supervised Label-Efficient WSI Screening method, dubbed LESS, for cytological WSI analysis with only slide-level labels, which can be effectively applied to small datasets. First, we suggest using variational positive-unlabeled (VPU) learning to uncover hidden labels of both benign and malignant patches. We provide appropriate supervision by using slide-level labels to improve the learning of patch-level features. Next, we take into account the sparse and random arrangement of cells in cytological WSIs. To address this, we propose a strategy to crop patches at multiple scales and utilize a cross-attention vision transformer (CrossViT) to combine information from different scales for WSI classification. The combination of our two steps achieves task-alignment, improving effectiveness and efficiency. We validate the proposed label-efficient method on a urine cytology WSI dataset encompassing 130 samples (13,000 patches) and FNAC 2019 dataset with 212 samples (21,200 patches). The experiment shows that the proposed LESS reaches 84.79%, 85.43%, 91.79% and 78.30% on a urine cytology WSI dataset, and 96.88%, 96.86%, 98.95%, 97.06% on FNAC 2019 dataset in terms of accuracy, AUC, sensitivity and specificity. It outperforms state-of-the-art MIL methods on pathology WSIs and realizes automatic cytological WSI cancer screening.Comment: This paper was submitted to Medical Image Analysis. It is under revie

    Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery

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    Minimally invasive surgery is playing an increasingly important role for patient care. Whilst its direct patient benefit in terms of reduced trauma, improved recovery and shortened hospitalisation has been well established, there is a sustained need for improved training of the existing procedures and the development of new smart instruments to tackle the issue of visualisation, ergonomic control, haptic and tactile feedback. For endoscopic intervention, the small field of view in the presence of a complex anatomy can easily introduce disorientation to the operator as the tortuous access pathway is not always easy to predict and control with standard endoscopes. Effective training through simulation devices, based on either virtual reality or mixed-reality simulators, can help to improve the spatial awareness, consistency and safety of these procedures. This thesis examines the use of endoscopic videos for both simulation and navigation purposes. More specifically, it addresses the challenging problem of how to build high-fidelity subject-specific simulation environments for improved training and skills assessment. Issues related to mesh parameterisation and texture blending are investigated. With the maturity of computer vision in terms of both 3D shape reconstruction and localisation and mapping, vision-based techniques have enjoyed significant interest in recent years for surgical navigation. The thesis also tackles the problem of how to use vision-based techniques for providing a detailed 3D map and dynamically expanded field of view to improve spatial awareness and avoid operator disorientation. The key advantage of this approach is that it does not require additional hardware, and thus introduces minimal interference to the existing surgical workflow. The derived 3D map can be effectively integrated with pre-operative data, allowing both global and local 3D navigation by taking into account tissue structural and appearance changes. Both simulation and laboratory-based experiments are conducted throughout this research to assess the practical value of the method proposed

    Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields

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    International audienceWe propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations

    Role of machine learning in early diagnosis of kidney diseases.

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    Machine learning (ML) and deep learning (DL) approaches have been used as indispensable tools in modern artificial intelligence-based computer-aided diagnostic (AIbased CAD) systems that can provide non-invasive, early, and accurate diagnosis of a given medical condition. These AI-based CAD systems have proven themselves to be reproducible and have the generalization ability to diagnose new unseen cases with several diseases and medical conditions in different organs (e.g., kidneys, prostate, brain, liver, lung, breast, and bladder). In this dissertation, we will focus on the role of such AI-based CAD systems in early diagnosis of two kidney diseases, namely: acute rejection (AR) post kidney transplantation and renal cancer (RC). A new renal computer-assisted diagnostic (Renal-CAD) system was developed to precisely diagnose AR post kidney transplantation at an early stage. The developed Renal-CAD system perform the following main steps: (1) auto-segmentation of the renal allograft from surrounding tissues from diffusion weighted magnetic resonance imaging (DW-MRI) and blood oxygen level-dependent MRI (BOLD-MRI), (2) extraction of image markers, namely: voxel-wise apparent diffusion coefficients (ADCs) are calculated from DW-MRI scans at 11 different low and high b-values and then represented as cumulative distribution functions (CDFs) and extraction of the transverse relaxation rate (R2*) values from the segmented kidneys using BOLD-MRI scans at different echotimes, (3) integration of multimodal image markers with the associated clinical biomarkers, serum creatinine (SCr) and creatinine clearance (CrCl), and (4) diagnosing renal allograft status as nonrejection (NR) or AR by utilizing these integrated biomarkers and the developed deep learning classification model built on stacked auto-encoders (SAEs). Using a leaveone- subject-out cross-validation approach along with SAEs on a total of 30 patients with transplanted kidney (AR = 10 and NR = 20), the Renal-CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal-CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified 10-fold cross-validation approach, the Renal-CAD system demonstrated its reproduciblity and robustness with a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88. In addition, a new renal cancer CAD (RC-CAD) system for precise diagnosis of RC at an early stage was developed, which incorporates the following main steps: (1) estimating the morphological features by applying a new parametric spherical harmonic technique, (2) extracting appearance-based features, namely: first order textural features are calculated and second order textural features are extracted after constructing the graylevel co-occurrence matrix (GLCM), (3) estimating the functional features by constructing wash-in/wash-out slopes to quantify the enhancement variations across different contrast enhanced computed tomography (CE-CT) phases, (4) integrating all the aforementioned features and modeling a two-stage multilayer perceptron artificial neural network (MLPANN) classifier to classify the renal tumor as benign or malignant and identify the malignancy subtype. On a total of 140 RC patients (malignant = 70 patients (ccRCC = 40 and nccRCC = 30) and benign angiomyolipoma tumors = 70), the developed RC-CAD system was validated using a leave-one-subject-out cross-validation approach. The developed RC-CAD system achieved a sensitivity of 95.3% ± 2.0%, a specificity of 99.9% ± 0.4%, and Dice similarity coefficient of 0.98 ± 0.01 in differentiating malignant from benign renal tumors, as well as an overall accuracy of 89.6% ± 5.0% in the sub-typing of RCC. The diagnostic abilities of the developed RC-CAD system were further validated using a randomly stratified 10-fold cross-validation approach. The results obtained using the proposed MLP-ANN classification model outperformed other machine learning classifiers (e.g., support vector machine, random forests, and relational functional gradient boosting) as well as other different approaches from the literature. In summary, machine and deep learning approaches have shown potential abilities to be utilized to build AI-based CAD systems. This is evidenced by the promising diagnostic performance obtained by both Renal-CAD and RC-CAD systems. For the Renal- CAD, the integration of functional markers extracted from multimodal MRIs with clinical biomarkers using SAEs classification model, potentially improved the final diagnostic results evidenced by high accuracy, sensitivity, and specificity. The developed Renal-CAD demonstrated high feasibility and efficacy for early, accurate, and non-invasive identification of AR. For the RC-CAD, integrating morphological, textural, and functional features extracted from CE-CT images using a MLP-ANN classification model eventually enhanced the final results in terms of accuracy, sensitivity, and specificity, making the proposed RC-CAD a reliable noninvasive diagnostic tool for RC. The early and accurate diagnosis of AR or RC will help physicians to provide early intervention with the appropriate treatment plan to prolong the life span of the diseased kidney, increase the survival chance of the patient, and thus improve the healthcare outcome in the U.S. and worldwide

    CAD system for early diagnosis of diabetic retinopathy based on 3D extracted imaging markers.

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    This dissertation makes significant contributions to the field of ophthalmology, addressing the segmentation of retinal layers and the diagnosis of diabetic retinopathy (DR). The first contribution is a novel 3D segmentation approach that leverages the patientspecific anatomy of retinal layers. This approach demonstrates superior accuracy in segmenting all retinal layers from a 3D retinal image compared to current state-of-the-art methods. It also offers enhanced speed, enabling potential clinical applications. The proposed segmentation approach holds great potential for supporting surgical planning and guidance in retinal procedures such as retinal detachment repair or macular hole closure. Surgeons can benefit from the accurate delineation of retinal layers, enabling better understanding of the anatomical structure and more effective surgical interventions. Moreover, real-time guidance systems can be developed to assist surgeons during procedures, improving overall patient outcomes. The second contribution of this dissertation is the introduction of a novel computeraided diagnosis (CAD) system for precise identification of diabetic retinopathy. The CAD system utilizes 3D-OCT imaging and employs an innovative approach that extracts two distinct features: first-order reflectivity and 3D thickness. These features are then fused and used to train and test a neural network classifier. The proposed CAD system exhibits promising results, surpassing other machine learning and deep learning algorithms commonly employed in DR detection. This demonstrates the effectiveness of the comprehensive analysis approach employed by the CAD system, which considers both low-level and high-level data from the 3D retinal layers. The CAD system presents a groundbreaking contribution to the field, as it goes beyond conventional methods, optimizing backpropagated neural networks to integrate multiple levels of information effectively. By achieving superior performance, the proposed CAD system showcases its potential in accurately diagnosing DR and aiding in the prevention of vision loss. In conclusion, this dissertation presents novel approaches for the segmentation of retinal layers and the diagnosis of diabetic retinopathy. The proposed methods exhibit significant improvements in accuracy, speed, and performance compared to existing techniques, opening new avenues for clinical applications and advancements in the field of ophthalmology. By addressing future research directions, such as testing on larger datasets, exploring alternative algorithms, and incorporating user feedback, the proposed methods can be further refined and developed into robust, accurate, and clinically valuable tools for diagnosing and monitoring retinal diseases

    Precision Monitoring for Disease Progression in Patients with Multiple Sclerosis: A Deep Learning Approach

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    Artificial intelligence has tremendous potential in a range of clinical applications. Leveraging recent advances in deep learning, the works in this thesis has generated a range of technologies for patients with Multiple Sclerosis (MS) that facilitate precision monitoring using routine MRI and clinical assessments; and contribute to realising the goal of personalised disease management. MS is a chronic inflammatory demyelinating disease of the central nervous system (CNS), characterised by focal demyelinating plaques in the brain and spinal cord; and progressive neurodegeneration. Despite success in cohort studies and clinical trials, the measurement of disease activity using conventional imaging biomarkers in real-world clinical practice is limited to qualitative assessment of lesion activity, which is time consuming and prone to human error. Quantitative measures, such as T2 lesion load, volumetric assessment of lesion activity and brain atrophy, are constrained by challenges associated with handling real-world data variances. In this thesis, DeepBVC was developed for robust brain atrophy assessment through imaging synthesis, while a lesion segmentation model was developed using a novel federated learning framework, Fed-CoT, to leverage large data collaborations. With existing quantitative brain structural analyses, this work has developed an effective deep learning analysis pipeline, which delivers a fully automated suite of MS-specific clinical imaging biomarkers to facilitate the precision monitoring of patients with MS and response to disease modifying therapy. The framework for individualised MRI-guided management in this thesis was complemented by a disease prognosis model, based on a Large Language Model, providing insights into the risks of clinical worsening over the subsequent 3 years. The value and performance of the MS biomarkers in this thesis are underpinned by extensive validation in real-world, multi-centre data from more than 1030 patients
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