arXiv subjects

Andras Jakab

Publications and source records attributed to Andras Jakab.

24 records · Page 2Linked to original sources

arXiv2021

The Federated Tumor Segmentation (FeTS) Challenge

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image analysis methods. However, the actual performance of participating (even the winning) algorithms on "real-world" clinical data often remains unclear, as the data included in challenges are usually acquired in very controlled settings at few institutions. The seemingly obvious solution of just collecting increasingly more data from more institutions in such challenges does not scale well due to privacy and ownership hurdles. Towards alleviating these concerns, we are proposing the FeTS challenge 2021 to cater towards both the development and the evaluation of models for the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Specifically, the FeTS 2021 challenge uses clinically acquired, multi-institutional magnetic resonance imaging (MRI) scans from the BraTS 2020 challenge, as well as from various remote independent institutions included in the collaborative network of a real-world federation (https://www.fets.ai/). The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i.e. on data from institutional distributions that were not part of the training datasets.

eess.IV↗

arXiv2020

Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels

Segmentation of the developing fetal brain is an important step in quantitative analyses. However, manual segmentation is a very time-consuming task which is prone to error and must be completed by highly specialized indi-viduals. Super-resolution reconstruction of fetal MRI has become standard for processing such data as it improves image quality and resolution. However, dif-ferent pipelines result in slightly different outputs, further complicating the gen-eralization of segmentation methods aiming to segment super-resolution data. Therefore, we propose using transfer learning with noisy multi-class labels to automatically segment high resolution fetal brain MRIs using a single set of seg-mentations created with one reconstruction method and tested for generalizability across other reconstruction methods. Our results show that the network can auto-matically segment fetal brain reconstructions into 7 different tissue types, regard-less of reconstruction method used. Transfer learning offers some advantages when compared to training without pre-initialized weights, but the network trained on clean labels had more accurate segmentations overall. No additional manual segmentations were required. Therefore, the proposed network has the potential to eliminate the need for manual segmentations needed in quantitative analyses of the fetal brain independent of reconstruction method used, offering an unbiased way to quantify normal and pathological neurodevelopment.

eess.IV↗

arXiv2019

Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair

Open spina bifida (SB) is one of the most common congenital defects and can lead to impaired brain development. Emerging fetal surgery methods have shown considerable success in the treatment of patients with this severe anomaly. Afterwards, alterations in the brain development of these fetuses have been observed. Currently no longitudinal studies exist to show the effect of fetal surgery on brain development. In this work, we present a fetal MRI neuro-imaging analysis pipeline for fetuses with SB, including automated fetal ventricle segmentation and deformation-based morphometry, and demonstrate its applicability with an analysis of ventricle enlargement in fetuses with SB. Using a robust super-resolution algorithm, we reconstructed fetal brains at both pre-operative and post-operative time points and trained a U-Net CNN in order to automatically segment the ventricles. We investigated the change of ventricle shape post-operatively, and the impacts of lesion size, type, and GA at operation on the change in ventricle shape. No impact was found, except for moderately larger ventriculomegaly progression in myeloschisis patients. Prenatal ventricle volume growth was also investigated. Our method allows for the quantification of longitudinal morphological changes to fully quantify the impact of prenatal SB repair and could be applied to predict postnatal outcomes.

eess.IV↗

arXiv2019

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

cs.CV↗

arXiv2019

Postoperative brain volumes are associated with one-year neurodevelopmental outcome in children with severe congenital heart disease

Children with congenital heart disease (CHD) remain at risk for neurodevelopmental impairment despite improved peri- and intraoperative care. Our prospective cohort study aimed to determine the relationship between perioperative brain volumes and neurodevelopmental outcome in neonates with severe CHD. Pre- and postoperative cerebral MRI was acquired in term born neonates with CHD undergoing neonatal cardiopulmonary bypass surgery. Brain volumes were measured using an atlas prior based automated method. One-year outcome was assessed with the Bayley-III. CHD infants (n=77) had lower pre- and postoper-ative total and regional brain volumes compared to controls (n=44, all p<0.01). CHD infants had poorer cognitive and motor outcome (p<=0.0001) and a trend towards lower language composite score compared to controls (p=0.06). The total and selected regional postoperative brain volumes predicted cognitive and language outcome (all p<0.04). This association was independent of length of intensive care unit stay for total, cortical, temporal, frontal and cerebellar volumes. In CHD neonates undergoing cardiopulmonary bypass surgery, pre- and postoperative brain volumes are reduced, and postoperative brain volumes predict cognitive and language outcome at one year. Reduced cerebral volumes in CHD patients could serve as a biomarker for im-paired outcome.

q-bio.NC↗

arXiv2018

Implicit Modeling with Uncertainty Estimation for Intravoxel Incoherent Motion Imaging

Intravoxel incoherent motion (IVIM) imaging allows contrast-agent free in vivo perfusion quantification with magnetic resonance imaging (MRI). However, its use is limited by typically low accuracy due to low signal-to-noise ratio (SNR) at large gradient encoding magnitudes as well as dephasing artefacts caused by subject motion, which is particularly challenging in fetal MRI. To mitigate this problem, we propose an implicit IVIM signal acquisition model with which we learn full posterior distribution of perfusion parameters using artificial neural networks. This posterior then encapsulates the uncertainty of the inferred parameter estimates, which we validate herein via numerical experiments with rejection-based Bayesian sampling. Compared to state-of-the-art IVIM estimation method of segmented least-squares fitting, our proposed approach improves parameter estimation accuracy by 65% on synthetic anisotropic perfusion data. On paired rescans of in vivo fetal MRI, our method increases repeatability of parameter estimation in placenta by 46%.

cs.CV↗