A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer's disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.

Fully automatic segmentation of 4D MRI for cardiac functional measurements

PURPOSE

Segmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three-dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four-dimensional, 4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D + t) cardiac MR images.

METHODS

The proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary.

RESULTS

This automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62 ± 5.47%, 87.35 ± 7.26%, and 82.63 ± 6.22% for the LV endocardial, LV epicardial, and RV contours, respectively.

CONCLUSIONS

We have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.

Towards optimal flow diverter porosity for the treatment of intracranial aneurysm

PURPOSE

Low-porosity endovascular stents, known as flow diverters (FDs), have been proposed as an effective and minimally invasive treatment for sidewall intracranial aneurysms (IAs). Although it has been reported that the efficacy of a FD is substantially influenced by its porosity, clinical doctors would clearly prefer to do their interventions optimally based on refined quantitative data. This study focuses on the association between the porosity configurations and the FD efficacy, in order to provide practical data to help the clinical doctors optimize the interventions.

METHOD

Numerical simulations in fluid dynamics were performed using four patient-specific IA geometries, pulsatile velocity profiles and braided fully resolved FDs. The variation of velocity and wall shear stress within the IAs, were investigated in this study. Lattice Boltzmann method (LBM) was used to solve the main challenge centered on the diversity of spatial scales since the typical diameter of struts of FDs is only 25μm while the artery normally can be larger by a hundred times.

RESULTS

Numerical simulations revealed that the blood flow within IA sac was substantially reduced when the porosity is less than 86%. In particular, the flow condition within each IA sac is favorite to initialize thrombus formation when porosity is less than 70%.

CONCLUSION

Our study suggests the existence of a porosity threshold below which the efficacy of a FD will be sufficient for the patients to initialize the thrombus formation. Therefore, by estimating the porosity of FD on patient-specific information, it may be potentially to predict whether or the blood flow condition will successfully become prothrombotic after the FD intervention.