Biology subjects

Rachmadi, M. F.

Publications and source records attributed to Rachmadi, M. F..

8 recordsLinked to original sources

bioRxiv2022

Improving the prediction of white matter hyperintensities evolution in brain MRI of patients with small vessel disease using stroke lesions information

Predicting the evolution of white matter hyperintensities (WMH), a common feature in brain magnetic resonance imaging (MRI) scans of older adults (i.e., whether WMH will grow, remain stable, or shrink with time) is important for personalised therapeutic interventions. However, this task is difficult mainly due to the myriad of vascular risk factors and comorbidities that influence it, and the low specificity and sensitivity of the image intensities and textures alone for predicting WMH evolution. Given the predominantly vascular nature of WMH, in this study, we evaluate the impact of incorporating stroke lesion information to a probabilistic deep learning model to predict the evolution of WMH 1-year after the baseline image acquisition, taken soon after a mild stroke event, using T2-FLAIR brain MRI. The Probabilistic U-Net was chosen for this study due to its capability of simulating and quantifying the uncertainties involved in the prediction of WMH evolution. We propose to use an additional loss called volume loss to train our model, and incorporate stroke lesions information, an influential factor in WMH evolution. Our experiments showed that jointly segmenting the disease evolution map (DEM) of WMH and stroke lesions, improved the accuracy of the DEM representing WMH evolution. The combination of introducing the volume loss and joint segmentation of DEM of WMH and stroke lesions outperformed other model configurations with mean volumetric absolute error of 0.0092 ml (down from 1.7739 ml) and 0.47% improvement on average Dice similarity coefficient in shrinking, growing and stable WMH.

bioinformatics↗

bioRxiv2022

MiniVess: A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging

We present MiniVess, the first annotated dataset of rodent cerebrovasculature, acquired using two-photon fluorescence microscopy. MiniVess consists of 70 3D image volumes with segmented ground truths. Segmentations were created using traditional image processing operations, a U-Net, and manual proofreading. Code for image preprocessing steps and the U-Net are provided. Supervised machine learning methods have been widely used for automated image processing of biomedical images. While much emphasis has been placed on the development of new network architectures and loss functions, there has been an increased emphasis on the need for publicly available annotated, or segmented, datasets. Annotated datasets are necessary during model training and validation. In particular, datasets that are collected from different labs are necessary to test the generalizability of models. We hope this dataset will be helpful in testing the reliability of machine learning tools for analyzing biomedical images.

neuroscience↗

bioRxiv2022

The Brain/MINDS Marmoset Connectivity Atlas: Exploring bidirectional tracing and tractography in the same stereotaxic space

We report on the implementation and features of the Brain/MINDS Marmoset Connectivity Atlas, BMCA, a new resource that provides access to anterograde neuronal tracer data in the prefrontal cortex of a marmoset brain. Neuronal tracers combined with fluorescence microscopy are a key technology for the systematic mapping of structural brain connectivity. We selected the prefrontal cortex for mapping due to its important role in higher brain functions. This work introduces the BMCA standard image preprocessing pipeline and tools for exploring and reviewing the data. We developed the BMCA-Explorer, which is an online image viewer designed for data exploration. Unlike other existing image explorers, it visualizes the data of different individuals in a common reference space at an unprecedented high resolution, facilitating comparative studies. To foster the integration with other marmoset brain image databases and cross-species comparisons, we added fiber tractography data from diffusion MRI, retrograde neural tracer data from the Marmoset Brain Connectivity Atlas project, and tools to map image data between marmoset and the human brain image space. This version of BMCA allows direct comparison between the results of 52 anterograde and 164 retrograde tracer injections in the cortex of the marmoset.

neuroscience↗

bioRxiv2019

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Previous studies have indicated that white matter hyperintensities (WMH), the main radiological feature of small vessel disease, may evolve (i.e., shrink, grow) or stay stable over a period of time. Predicting these changes are challenging because it involves some unknown clinical risk factors that leads to a non-deterministic prediction task. In this study, we propose a deep learning model to predict the evolution of WMH from baseline to follow-up (i.e., 1-year later), namely "Disease Evolution Predictor" (DEP) model, which can be adjusted to become a non-deterministic model. The DEP model receives a baseline image as input and produces a map called "Disease Evolution Map" (DEM), which represents the evolution of WMH from baseline to follow-up. Two DEP models are proposed, namely DEP-UResNet and DEP-GAN, which are representatives of the supervised (i.e., need expert-generated manual labels to generate the output) and unsupervised (i.e., do not require manual labels produced by experts) deep learning algorithms respectively. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we modulate a Gaussian noise array to the DEP model as auxiliary input. This forces the DEP model to imitate a wider spectrum of alternatives in the prediction results. The alternatives of using other types of auxiliary input instead, such as baseline WMH and stroke lesion loads are also proposed and tested. Based on our experiments, the fully supervised machine learning scheme DEP-UResNet regularly performed better than the DEP-GAN which works in principle without using any expert-generated label (i.e., unsupervised). However, a semi-supervised DEP-GAN model, which uses probability maps produced by a supervised segmentation method in the learning process, yielded similar performances to the DEP-UResNet and performed best in the clinical evaluation. Furthermore, an ablation study showed that an auxiliary input, especially the Gaussian noise, improved the performance of DEP models compared to DEP models that lacked the auxiliary input regardless of the models architecture. To the best of our knowledge, this is the first extensive study on modelling WMH evolution using deep learning algorithms, which deals with the non-deterministic nature of WMH evolution.

neuroscience↗

bioRxiv2019

Predicting the Evolution of White Matter Hyperintensities in Brain MRI using Generative Adversarial Networks and Irregularity Map

We propose a Generative Adversarial Network (GAN) model named Disease Evolution Predictor GAN (DEP-GAN) to predict the evolution (i.e., progression and regression) of White Matter Hyperintensities (WMH) in small vessel disease. In this study, the evolution of WMH is represented by the \"Disease Evolution Map\" (DEM) produced by subtracting irregularity map (IM) images from two time points: baseline and follow-up. DEP-GAN uses two discriminators (critics) to enforce anatomically realistic follow-up image and DEM. To simulate the non-deterministic and unknown parameters involved in WMH evolution, we propose modulating an array of random noises to the DEP-GANs generator which forces the model to imitate a wider spectrum of alternatives in the results. Our study shows that the use of two critics and random noises modulation in the proposed DEP-GAN improves its performance predicting the evolution of WMH in small vessel disease. DEP-GAN is able to estimate WMH volume in the follow-up year with mean (std) estimation error of -1.91 (12.12) ml and predict WMH evolution with mean rate of 72.01% accuracy (i.e., 88.69% and 23.92% better than Wasserstein GAN).

neuroscience↗

bioRxiv2019

Dilated Saliency U-Net for White Matter Hyperintensities Segmentation using Irregularity Age Map

White matter hyperintensities(WMH) appear as regions of abnormally high signal intensity on T2-weighted magnetic resonance image(MRI) sequences. In particular, WMH have been noteworthy in age-related neuroscience for being a crucial biomarker for Alzheimer s disease and brain aging processes. However, the automatic WMH segmentation is challenging because of the variable intensity range, size and shape. U-Net tackled this problem through the dense prediction and showed competitive performances on not only WMH segmentation/detection but also on varied image segmentation tasks, but it still accompanies a high complexity of the network architecture. In this study, we propose to use Saliency U-Net architecture and irregularity age map(IAM) to decrease the U-Net complexity without a performance loss. We trained Saliency U-Net using both T2-FLAIR MRI sequence and IAM. Since IAM guides where irregularities, in which WMH is possibly included, exist on the MRI slice, Saliency U-Net performs better than the original U-Net trained only using T2-FLAIR. The better performance was achieved with fewer parameters and shorter training time. Moreover, the application of dilated convolution enhanced Saliency U-Net to recognise the shape of large WMH more accurately by learning multi-context on MRI slices. This network named Dilated Saliency U-Net improved Dice coefficient score to 0.5588 which is the best score among our experimental models, and recorded a relatively good sensitivity of 0.4747 with the shortest train time and the least number of parameters. In conclusion, based on the experimental results, incorporating IAM through Dilated Saliency U-Net resulted an appropriate approach for WMH segmentation.

neuroscience↗

bioRxiv2018

Transfer Learning for Task Adaptation of Brain Lesion Assessment and Prediction of Brain Abnormalities Progression/Regression using Irregularity Age Map in Brain MRI

The Irregularity Age Map (IAM) for the unsupervised assessment of brain white matter hyperintensities (WMH) opens several opportunities in machine learning-based brain MRI analysis, including transfer task adaptation learning in the MRI brain lesions segmentation and prediction of lesion progression and regression. The lack of need for manual labels is useful for transfer learning. Whereas, the nature of IAM itself can be exploited for predicting lesion progression/regression. In this study, we propose the use of task adaptation transfer learning for WMH segmentation using CNN through weakly-training UNet and UResNet using the output from IAM and the use of IAM for predicting patterns of WMH progression and regression.

neuroscience↗

bioRxiv2018

Limited One-time Sampling Irregularity Age Map (LOTS-IAM): Automatic Unsupervised Detection of Brain White Matter Abnormalities in Structural Magnetic Resonance Images

We present a complete study of limited one-time sampling irregularity map (LOTS-IM), a fully automatic unsupervised approach to extract brain tissue irregularities in magnetic resonance images (MRI), including its application and evaluation for quantitative assessment of white matter hyperintensities (WMH) of presumed vascular origin and assessing multiple sclerosis (MS) lesion progression. LOTS-IM is unique compared to similar other methods because it yields irregularity map (IM) which represents WMH as irregularity values, not probability values, and retains the original MRIs texture information. We tested and compared the usage of IM for WMH segmentation on T2-FLAIR MRI with various methods, including the well established unsupervised WMH segmentation Lesion Growth Algorithm from the public toolbox Lesion Segmentation Toolbox (LST-LGA), conventional supervised machine learning schemes and state-of-the-art supervised deep neural networks. In our experiments, LOTS-IM outperformed unsupervised method LST-LGA, both in performance and processing speed, thanks to the limited one-time sampling scheme and its implementation on GPU. Our method also outperformed supervised conventional machine learning algorithms (i.e., support vector machine (SVM) and random forest (RF)) and deep neural networks algorithms (i.e., deep Boltzmann machine (DBM) and convolutional encoder network (CEN)), while yielding comparable results to the convolutional neural network schemes that rank top of the algorithms developed up to date for this purpose (i.e., UResNet and UNet). The high sensitivity of IM on depicting signal change deems suitable for assessing MS progression, although care must be taken with signal changes not reflective of a true pathology.

neuroscience↗