Graph Domain Adaptation for Alignment-Invariant Brain Surface Segmentation

The varying cortical geometry of the brain creates numerous challenges for its analysis. Recent developments have enabled learning surface data directly across multiple brain surfaces via graph convolutions on cortical data. However, current graph learning algorithms do fail when brain surface data are misaligned across subjects, thereby affecting their ability to deal with data from multiple domains. Adversarial training is widely used for domain adaptation to improve the segmentation performance across domains. In this paper, adversarial training is exploited to learn surface data across inconsistent graph alignments. This novel approach comprises a segmentator that uses a set of graph convolution layers to enable parcellation directly across brain surfaces in a source domain, and a discriminator that predicts a graph domain from segmentations. More precisely, the proposed adversarial network learns to generalize a parcellation across both, source and target domains. We demonstrate an 8% mean improvement in performance over a non-adversarial training strategy applied on multiple target domains extracted from MindBoggle, the largest publicly available manually-labeled brain surface dataset

Effects of exercise on physical fitness and blood factors of addicted persons who have quitted drugs for two months.

BANITALEBI, E.; FARAMARZI, M.; NURI, R.; KHOSROZADEH, J.; GHAFOORIAN, M. Effect of exercise training on health-related physical fitness factors and blood lipids profile of former addicted persons. Brazilian Journal of Biomotricity, v. 4, n. 3, p. 190-197, 2010. Dysfunctional eating patterns and excessive weight gains have been observed during recovery from drug and alcohol addictions. The purpose of this study was to determine the effect of exercise training on health-related physical fitness factors and blood lipids profile of former addicted persons. Thirty seven males who were 23-49 years old, and had one-year quitting history were selected and randomized (exercise group, n= 18 and control, n= 19). Thirty eight individuals completed the entire study; 16 persons were in exercise group and 15 persons were in control group. Exercise training was consisted primarily of some game-based aerobic exercise. Exercise training duration progressed from 20 minutes at the baseline to 45 minutes at the end of weeks 12th, and intensity of exercise progressed from 50% of heart rate reserve of baseline to 70 % at 12 weeks. Weight, BMI and WHR were measured. Muscle endurance, flexibility and Vo2Peak were measured using by pull up, Sit -and -Rich test and one-mile Rockport walk test, respectively. Body composition was assessed using the sum of three skin-fold measurement specific for males (chest, abdomen, and tight). Total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and triglyceride (TG) were measured enzymatically using diagnostic Pars kits. All variables were measured at baseline. Data analyzed by using ANCOVA analysis. There were no significant differences in weight (p=0.208), BMI (P=0.2631), CT (P=0.428), HDL (0.833), LDL (0.396), VLDL (P=0.169), TG (P=0.283), Vo2peak (p=0.884), flexibility (P=0.923) and Pull-up (P=0.44) after 12 weeks exercise training between two groups, but there was significant difference in WHR (p=0.044). It appears that, exercise training can prevent weight gain after quitting drugs and substances

Effects of β -hydroxy- β -methylbutyrate on kidney parameters and body composition in untrained males after 8 weeks combination resistance training

زمینه و هدف: مطالعات علمی دقیق در مورد بی خطر بودن بتاهیدروکسی بتامتیلبوتیرات (HMB) در انسان تاکنون انجام نشده است و تنها مطالعات اندکی در مورد تاثیر این مکمل بر شاخص های مرتبط با سلامت در حیوانات انجام شده است. این مطالعه با هدف بررسی تاثیر 8 هفته تمرین مقاومتی به همراه مصرف مکمل بتا- هیدروکسی بتا- متیل بوتیرات (HMB) بر شاخص های کارکرد کلیوی و ترکیب بدنی مردان غیر ورزشکار انجام شد. روش بررسی: در این کارآزمایی بالینی 24 دانشجوی پسر غیر ورزشکار انتخاب و به صورت تصادفی به دو گروه کنترل (دارو نما، 14نفر) و تجربی (مکمل، 10 نفر) تقسیم شدند. هر دو گروه 8 هفته تمرین مقاومتی را به صورت 3 جلسه در هفته، اجرا کردند. از آزمودنی ها یک روز قبل و 2 روز بعد از برنامه تمرینی، نمونه خون و ادرار در حالت ناشتا گرفته شد و برای تمام نمونه ها اوره و کراتینین اندازه گیری و میزان فیلتراسیون گلومرولی نیز محاسبه گردید. ترکیب بدنی، وزن آزمودنی ها و قدرت بالا تنه و پایین تنه اندازه گیری شد. تجزیه و تحلیل داده ها با استفاده از آزمون های آماری t زوجی و t مستقل انجام گرفت. یافته ها: گروه مکمل افزایش معنادار در توده خالص بدنی (002/0P=) و کاهش معناداری در توده چربی بدن (006/0P=) داشت. همچنین، مکمل سبب کاهش معنادار غلظت ازت اوره ادرار (036/0P=) شد ولی تاثیر معناداری بر غلظت ازت اوره خون، کراتینین خون، کراتینین ادرار و میزان فیلتراسیون گلومرولی نداشت. نتیجه گیری: مصرف مکمل بتا- هیدروکسی بتا- متیل بوتیرات باعث افزایش توده خالص بدنی و قدرت یک تکرار بیشینه و کاهش چربی بدن در افراد غیر ورزشکار می شود. ولی تاثیر زیان آوری بر عملکرد کلیوی در مدت مصرف شده در این مطالعه ندارد

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

An Inductive Transfer Learning Approach using Cycle-consistent Adversarial Domain Adaptation with Application to Brain Tumor Segmentation

With recent advances in supervised machine learning for medical image analysis applications, the annotated medical image datasets of various domains are being shared extensively. Given that the annotation labelling requires medical expertise, such labels should be applied to as many learning tasks as possible. However, the multi-modal nature of each annotated image renders it difficult to share the annotation label among diverse tasks. In this work, we provide an inductive transfer learning (ITL) approach to adopt the annotation label of the source domain datasets to tasks of the target domain datasets using Cycle-GAN based unsupervised domain adaptation (UDA). To evaluate the applicability of the ITL approach, we adopted the brain tissue annotation label on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the task of brain tumor segmentation on the target domain dataset of MRI. The results confirm that the segmentation accuracy of brain tumor segmentation improved significantly. The proposed ITL approach can make significant contribution to the field of medical image analysis, as we develop a fundamental tool to improve and promote various tasks using medical images

Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

Magnetic Resonance Imaging (MRI) is widely used in routine clinical diagnosis and treatment. However, variations in MRI acquisition protocols result in different appearances of normal and diseased tissue in the images. Convolutional neural networks (CNNs), which have shown to be successful in many medical image analysis tasks, are typically sensitive to the variations in imaging protocols. Therefore, in many cases, networks trained on data acquired with one MRI protocol, do not perform satisfactorily on data acquired with different protocols. This limits the use of models trained with large annotated legacy datasets on a new dataset with a different domain which is often a recurring situation in clinical settings. In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?; and, 2) What portion of the pre-trained model parameters should be retrained given a certain number of the new domain training samples? To address these questions, we conducted extensive experiments in white matter hyperintensity segmentation task. We trained a CNN on legacy MR images of brain and evaluated the performance of the domain-adapted network on the same task with images from a different domain. We then compared the performance of the model to the surrogate scenarios where either the same trained network is used or a new network is trained from scratch on the new dataset.The domain-adapted network tuned only by two training examples achieved a Dice score of 0.63 substantially outperforming a similar network trained on the same set of examples from scratch.Comment: 8 pages, 3 figure

Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data

The evaluation of white matter lesion progression is an important biomarker in the follow-up of MS patients and plays a crucial role when deciding the course of treatment. Current automated lesion segmentation algorithms are susceptible to variability in image characteristics related to MRI scanner or protocol differences. We propose a model that improves the consistency of MS lesion segmentations in inter-scanner studies. First, we train a CNN base model to approximate the performance of icobrain, an FDA-approved clinically available lesion segmentation software. A discriminator model is then trained to predict if two lesion segmentations are based on scans acquired using the same scanner type or not, achieving a 78% accuracy in this task. Finally, the base model and the discriminator are trained adversarially on multi-scanner longitudinal data to improve the inter-scanner consistency of the base model. The performance of the models is evaluated on an unseen dataset containing manual delineations. The inter-scanner variability is evaluated on test-retest data, where the adversarial network produces improved results over the base model and the FDA-approved solution.Comment: MICCAI BrainLes 2019 Worksho

Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2019. Published at https://doi.org/10.1007/978-3-030-27272-2_29 Code: https://github.com/zoujx96/DR-segmentatio

Scribble-based Domain Adaptation via Co-segmentation

Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capability. To be able to generalise from one domain (e.g. one imaging modality) to another, domain adaptation has to be performed. While supervised methods may lead to good performance, they require to fully annotate additional data which may not be an option in practice. In contrast, unsupervised methods don't need additional annotations but are usually unstable and hard to train. In this work, we propose a novel weakly-supervised method. Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation. This paper introduces a new formulation of domain adaptation based on structured learning and co-segmentation. Our method is easy to train, thanks to the introduction of a regularised loss. The framework is validated on Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method outperforms unsupervised approaches and achieves comparable performance to a fully-supervised approach.Comment: Accepted at MICCAI 202