System modeling and identification in indicator dilution method for assessment of ejection fraction and pulmonary blood volume

Clinically relevant cardiovascular parameters, such as pulmonary blood volume (PBV) and ejection fraction (EF), can be assessed through indicator dilution techniques. Among these techniques, which are typically invasive due to the need for central catheterization, contrast ultrasonography provides a new emerging minimally invasive option. PBV and EF are then measured by a dilution system identification algorithm after detection of multiple dilution curves by an ultrasound scanner. In this paper, dilution systems are represented by parametric models. Since the measured indicator dilution curves (IDCs) are corrupted by measurement artifacts and outliers, the use of conventional least square error (LSE) estimator for estimating system parameters is not optimal. Different estimators are therefore proposed for estimating the system parameters. Comparison of these estimators with the LSE estimator in assessing EF and PBV is performed on simulated, in vitro and patient data. The results show that the proposed total least absolute deviation estimator (TLAD) outperforms other estimators. The measured IDCs are highly corrupted by noise, which affect the estimation of EF and PBV. Therefore, a two stage denoising method capable of removing outliers is also proposed for removing noise in IDCs

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