Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores

Segmentation of Brain Tissues from Infant MRI Records Using Machine Learning Techniques

The automatic segmentation of medical images is an intensely investigated problem, due to the quick rise of medical image data amount created day by day, which cannot be followed by the number of human experts. This paper searches for the most suitable classical machine learning method to be employed in the segmentation of various tissue types from volumetric multi-spectral MRI records of 6-month infant patients. Model training and model based prediction is performed using the 10 records of the train data set available at the iSeg-2017 challenge. All MRI records are treated with histogram normalization and feature generation, and then fed to six machine learning methods, which use them as train and test data according to the leave-one-out technique. The output of the classification algorithms is evaluated with statistical methods. The best segmentation accuracy is achieved by the random forest based approach, with a correct decision rate of 83.4%

Effect of inversion time on the precision of myocardial late gadolinium enhancement quantification evaluated with synthetic inversion recovery MR imaging.

OBJECTIVES: To evaluate the influence of inversion time (TI) on the precision of myocardial late gadolinium enhancement (LGE) quantification using synthetic inversion recovery (IR) imaging in patients with myocardial infarction (MI). METHODS: Fifty-three patients with suspected prior MI underwent 1.5-T cardiac MRI with conventional magnitude (MagIR) and phase-sensitive IR (PSIR) LGE imaging and T1 mapping at 15 min post-contrast. T1-based synthetic MagIR and PSIR images were calculated with a TI ranging from -100 to +150 ms at 5-ms intervals relative to the optimal TI (TI0). LGE was quantified using a five standard deviation (5SD) and full width at half-maximum (FWHM) thresholds. Measurements were compared using one-way analysis of variance. RESULTS: The MagIRsy technique provided precise assessment of LGE area at TIs >/= TI0, while precision was decreased below TI0. The LGE area showed significant differences at </= -25 ms compared to TI0 using 5SD (P < 0.001) and at </= -65 ms using the FWHM approach (P < 0.001). LGE measurements did not show significant difference over the analysed TI range in the PSIRsy images using either of the quantification methods. CONCLUSIONS: T1 map-based PSIRsy images provide precise quantification of MI independent of TI at the investigated time point post-contrast. MagIRsy-based MI quantification is precise at TI0 and at longer TIs while showing decreased precision at TI values below TI0. KEY POINTS: * Synthetic IR imaging retrospectively generates LGE images at any theoretical TI * Synthetic IR imaging can simulate the effect of TI on LGE quantification * Fifteen minutes post-contrast MagIR sy accurately quantifies infarcts from TI 0 to TI 0 + 150 ms * Fifteen minutes post-contrast PSIR sy provides precise infarct size independent of TI * Synthetic IR imaging has further advantages in reducing operator dependence