An insight into the analogies, state-of-the-art technologies, concepts, and prospects under the umbrella of perovskite materials (both inorganic-organic hybrid halide perovskites and ferroelectric perovskites) for future multifunctional energy conversion and storage devices is provided. Often, these are considered entirely different branches of research; however, considering them simultaneously and holistically can provide several new opportunities. Recent advancements have highlighted the potential of hybrid perovskites for high-efficiency solar cells. The intrinsic polar properties of these materials, including the potential for ferroelectricity, provide additional possibilities for simultaneously exploiting several energy conversion mechanisms such as the piezoelectric, pyroelectric, and thermoelectric effect and electrical energy storage. The presence of these phenomena can support the performance of perovskite solar cells. The energy conversion using these effects (piezo-, pyro-, and thermoelectric effect) can also be enhanced by a change in the light intensity. Thus, there lies a range of possibilities for tuning the structural, electronic, optical, and magnetic properties of perovskites to simultaneously harvest energy using more than one mechanism to realize an improved efficiency. This requires a basic understanding of concepts, mechanisms, corresponding material properties, and the underlying physics involved with these effects.

Mutual Insight on Ferroelectrics and Hybrid Halide Perovskites: A Platform for Future Multifunctional Energy Conversion.

https://hal.archives-ouvertes.fr/hal-00551034/document

A review of segmentation methods in short axis cardiac MR images

Echocardiography has become the primary imaging tool in the diagnosis and assessment of congenital and acquired heart disease in infants, children, and adolescents. Transthoracic echocardiography (TTE) is an ideal tool for cardiac assessment, as it is noninvasive, portable, and efficacious in providing detailed anatomic, hemodynamic, and physiologic information about the pediatric heart. Recommendations for standards in the performance of a routine 2-dimensional (2D) echocardiogram, 1 fetal echocardiogram, pediatric transesophageal echocardiogram, intraoperative transesophageal echocardiogram, and stress echocardiogram exist, as do guidelines for appropriate training in various echocardiographic disciplines. As part of the accreditation process for echocardiography laboratories, the Intersocietal Commission for the Accreditation of Echocardiography Laboratories has established basic performance standards for pediatric TTE, 13 but no other standards document exists for the performance of a pediatric TTE. The pediatric echocardiogram is a unique examination with features that distinguish it from other echocardiograms. There is a wide spectrum of anomalies encountered in patients with congenital heart disease. Certain views are of added importance

http://www.tchp.us/sites/default/uploadedfiles/For_Providers/Provider_Resources/Practice_Guidelines/Pediatric_Echocardiogram_Guidelines.pdf

Guidelines and standards for performance of a pediatric echocardiogram: a report from the Task Force of the Pediatric Council of the American Society of Echocardiography.

https://escholarship.org/content/qt3hm7819r/qt3hm7819r.pdf?t=om50sb

A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI.

Most cancers in humans are large, measuring centimetres in diameter, and composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is that virtually every neoplastic cell within a large tumour often contains the same core set of genetic alterations, with heterogeneity confined to mutations that emerge late during tumour growth. How such alterations expand within the spatially constrained three-dimensional architecture of a tumour, and come to dominate a large, pre-existing lesion, has been unclear. Here we describe a model for tumour evolution that shows how short-range dispersal and cell turnover can account for rapid cell mixing inside the tumour. We show that even a small selective advantage of a single cell within a large tumour allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides insights into spatial and temporal aspects of tumour growth, but also suggests that targeting short-range cellular migratory activity could have marked effects on tumour growth rates.

A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity

LOGISMOS (Layered Optimal Graph Image Segmentation for Multiple Objects and Surfaces) is a general approach for optimally segmenting multiple n-D surfaces that mutually interact within individual and/or between object. JEI (Just-Enough Interaction) extension enables the users to intuitively and efficiently guide LOGISMOS to achieve clinically acceptable segmentation that still maintains global optimality, embedded shape, and anatomy knowledge. This chapter is aimed at both the conceptual designers of quantitative medical image analysis methods and developers adopting LOGISMOS-JEI to perform new segmentation tasks. The basic principles and essential techniques of LOGISMOS-JEI are presented. Using three clinical segmentation problems, the process of designing LOGISMOS-JEI applications is described with a focus on choosing proper techniques at all stages of the analysis pipeline. The capability of JEI is demonstrated by reporting the achieved segmentation accuracy and quantifying the levels of human effort involved.

LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311-0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.

https://www.mdpi.com/2072-6694/13/11/2568/pdf

Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Automated and accurate segmentation of the aorta in 3D+time MR image data is important for early detection of connective tissue disorders leading to aortic aneurysms and dissections. A computer-aided diagnosis method is reported that allows the objective identification of subjects with connective tissue disorders from two-phase 3D+time aortic MR images. Our automated segmentation method combines level-set and optimal border detection. The resulting aortic lumen surface was registered with an aortic model followed by calculation of modal indices of aortic shape and motion. The modal indices reflect the differences of any individual aortic shape and motion from an average aortic behavior. The indices were input to a Support Vector Machine (SVM) classifier and a discrimination model was constructed. 3D+time MR image data sets acquired from 22 normal and connective tissue disorder subjects at end-diastole (R-wave peak) and at 45% of the R-R interval were used to evaluate the performance of our method. The automated 3D segmentation result produced accurate aortic surfaces covering the aorta from the left-ventricular outflow tract to the diaphragm and yielded subvoxel accuracy with signed surface positioning errors of -0.09±1.21 voxel (-0.15±2.11 mm). The computer aided diagnosis method distinguished between normal and connective tissue disorder subjects with a classification correctness of 90.1 %.

Quantitative analysis of two-phase 3D+time aortic MR images

Quantitative analysis of the left ventricular shape and motion patterns associated with left ventricular mechanical
dyssynchrony (LVMD) is essential for diagnosis and treatment planning in congestive heart failure Real-time
3D echocardiography (RT3DE) used for LVMD analysis is frequently limited by heavy speckle noise or partially
incomplete data, thus a segmentation method utilizing learned global shape knowledge is beneficial In this
study, the endocardial surface of the left ventricle (LV) is segmented using a hybrid approach combining active
shape model (ASM) with optimal graph search The latter is used to achieve landmark refinement in the ASM
framework Optimal graph search translates the 3D segmentation into the detection of a minimum-cost closed set
in a graph and can produce a globally optimal result Various information-gradient, intensity distributions,
and regional-property terms-are used to define the costs for the graph search The developed method was
tested on 44 RT3DE datasets acquired from 26 LVMD patients The segmentation accuracy was assessed by
surface positioning error and volume overlap measured for the whole LV as well as 16 standard LV regions The
segmentation produced very good results that were not achievable using ASM or graph search alone

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

Conventional analysis of cardiac ventricular magnetic resonance images is performed using short axis images and does not guarantee completeness and consistency of the ventricle coverage. In this paper, a four-dimensional (4D, 3D+time) left and right ventricle statistical shape model was generated from the combination of the long axis and short axis images. Iterative mutual intensity registration and interpolation were used to merge the long axis and short axis images into isotropic 4D images and simultaneously correct existing breathing artifact. Distance-based shape interpolation and approximation were used to generate complete ventricle shapes from the long axis and short axis manual segmentations. Landmarks were automatically generated and propagated to 4D data samples using rigid alignment, distance-based merging, and B-spline transform. Principal component analysis (PCA) was used in model creation and analysis. The two strongest modes of the shape model captured the most important shape feature of Tetralogy of Fallot (TOF) patients, right ventricle enlargement. Classification of cardiac images into classes of normal and TOF subjects performed on 3D and 4D models showed 100% classification correctness rates for both normal and TOF subjects using k -Nearest Neighbor ( k =1 or 3) classifier and the two strongest shape modes.

Honghai Zhang

Papers

LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction

Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters?

Quantitative analysis of two-phase 3D+time aortic MR images

Left-ventricle segmentation in real-time 3D echocardiography using a hybrid active shape model and optimal graph search approach

Analysis of four-dimensional cardiac ventricular magnetic resonance images using statistical models of ventricular shape and cardiac motion