VIPAR, a quantitative approach to 3D histopathology applied to lymphatic malformations.

BACKGROUND: Lack of investigatory and diagnostic tools has been a major contributing factor to the failure to mechanistically understand lymphedema and other lymphatic disorders in order to develop effective drug and surgical therapies. One difficulty has been understanding the true changes in lymph vessel pathology from standard 2D tissue sections. METHODS: VIPAR (volume information-based histopathological analysis by 3D reconstruction and data extraction), a light-sheet microscopy-based approach for the analysis of tissue biopsies, is based on digital reconstruction and visualization of microscopic image stacks. VIPAR allows semiautomated segmentation of the vasculature and subsequent nonbiased extraction of characteristic vessel shape and connectivity parameters. We applied VIPAR to analyze biopsies from healthy lymphedematous and lymphangiomatous skin. RESULTS: Digital 3D reconstruction provided a directly visually interpretable, comprehensive representation of the lymphatic and blood vessels in the analyzed tissue volumes. The most conspicuous features were disrupted lymphatic vessels in lymphedematous skin and a hyperplasia (4.36-fold lymphatic vessel volume increase) in the lymphangiomatous skin. Both abnormalities were detected by the connectivity analysis based on extracted vessel shape and structure data. The quantitative evaluation of extracted data revealed a significant reduction of lymphatic segment length (51.3% and 54.2%) and straightness (89.2% and 83.7%) for lymphedematous and lymphangiomatous skin, respectively. Blood vessel length was significantly increased in the lymphangiomatous sample (239.3%). CONCLUSION: VIPAR is a volume-based tissue reconstruction data extraction and analysis approach that successfully distinguished healthy from lymphedematous and lymphangiomatous skin. Its application is not limited to the vascular systems or skin. FUNDING: Max Planck Society, DFG (SFB 656), and Cells-in-Motion Cluster of Excellence EXC 1003

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previously used to improve segmentation accuracy, yet not been formulated in a consistent mathematical framework. Hence, we also propose a theoretical formulation of test-time augmentation, where a distribution of the prediction is estimated by Monte Carlo simulation with prior distributions of parameters in an image acquisition model that involves image transformations and noise. We compare and combine our proposed aleatoric uncertainty with model uncertainty. Experiments with segmentation of fetal brains and brain tumors from 2D and 3D Magnetic Resonance Images (MRI) showed that 1) the test-time augmentation-based aleatoric uncertainty provides a better uncertainty estimation than calculating the test-time dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions, and 2) our test-time augmentation outperforms a single-prediction baseline and dropout-based multiple predictions.Comment: 13 pages, 8 figures, accepted by NeuroComputin

Finding Nano-\"Otzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography

Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, however, cannot cope with its very low signal-to-noise ratio. In order to design more powerful transfer functions, we propose to leverage soft segmentation as an explicit component of visualization for noisy volumes. Our technical realization is based on semi-supervised learning where we combine the advantages of two segmentation algorithms. A first weak segmentation algorithm provides good results for propagating sparse user provided labels to other voxels in the same volume. This weak segmentation algorithm is used to generate dense pseudo labels. A second powerful deep-learning based segmentation algorithm can learn from these pseudo labels to generalize the segmentation to other unseen volumes, a task that the weak segmentation algorithm fails at completely. The proposed volume visualization uses the deep-learning based segmentation as a component for segmentation-aware transfer function design. Appropriate ramp parameters can be suggested automatically through histogram analysis. Finally, our visualization uses gradient-free ambient occlusion shading to further suppress visual presence of noise, and to give structural detail desired prominence. The cryo-ET data studied throughout our technical experiments is based on the highest-quality tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques

Uncertainty-aware Visualization in Medical Imaging - A Survey

Medical imaging (image acquisition, image transformation, and image visualization) is a standard tool for clinicians in order to make diagnoses, plan surgeries, or educate students. Each of these steps is affected by uncertainty, which can highly influence the decision-making process of clinicians. Visualization can help in understanding and communicating these uncertainties. In this manuscript, we aim to summarize the current state-of-the-art in uncertainty-aware visualization in medical imaging. Our report is based on the steps involved in medical imaging as well as its applications. Requirements are formulated to examine the considered approaches. In addition, this manuscript shows which approaches can be combined to form uncertainty-aware medical imaging pipelines. Based on our analysis, we are able to point to open problems in uncertainty-aware medical imaging

Natural ventilation design attributes application effect on, indoor natural ventilation performance of a double storey, single unit residential building

In establishing a good indoor thermal condition, air movement is one of the important parameter to be considered to provide indoor fresh air for occupants. Due to the public awareness on environment impact, people has been increasingly attentive to passive design in achieving good condition of indoor building ventilation. Throughout case studies, significant building attributes were found giving effect on building indoor natural ventilation performance. The studies were categorized under vernacular houses, contemporary houses with vernacular element and contemporary houses. The indoor air movement of every each spaces in the houses were compared with the outdoor air movement surrounding the houses to indicate the space’s indoor natural ventilation performance. Analysis found the wind catcher element appears to be the most significant attribute to contribute most to indoor natural ventilation. Wide opening was also found to be significant especially those with louvers. Whereas it is also interesting to find indoor layout design is also significantly giving impact on the performance. The finding indicates that a good indoor natural ventilation is not only dictated by having proper openings at proper location of a building, but also on how the incoming air movement is managed throughout the interior spaces by proper layout. Understanding on the air pressure distribution caused by indoor windward and leeward side is important in directing the air flow to desired spaces in producing an overall good indoor natural ventilation performance

Feature-driven Volume Visualization of Medical Imaging Data

Direct volume rendering (DVR) is a volume visualization technique that has been proved to be a very powerful tool in many scientific visualization domains. Diagnostic medical imaging is one such domain in which DVR provides new capabilities for the analysis of complex cases and improves the efficiency of image interpretation workflows. However, the full potential of DVR in the medical domain has not yet been realized. A major obstacle for a better integration of DVR in the medical domain is the time-consuming process to optimize the rendering parameters that are needed to generate diagnostically relevant visualizations in which the important features that are hidden in image volumes are clearly displayed, such as shape and spatial localization of tumors, its relationship with adjacent structures, and temporal changes in the tumors. In current workflows, clinicians must manually specify the transfer function (TF), view-point (camera), clipping planes, and other visual parameters. Another obstacle for the adoption of DVR to the medical domain is the ever increasing volume of imaging data. The advancement of imaging acquisition techniques has led to a rapid expansion in the size of the data, in the forms of higher resolutions, temporal imaging acquisition to track treatment responses over time, and an increase in the number of imaging modalities that are used for a single procedure. The manual specification of the rendering parameters under these circumstances is very challenging. This thesis proposes a set of innovative methods that visualize important features in multi-dimensional and multi-modality medical images by automatically or semi-automatically optimizing the rendering parameters. Our methods enable visualizations necessary for the diagnostic procedure in which 2D slice of interest (SOI) can be augmented with 3D anatomical contextual information to provide accurate spatial localization of 2D features in the SOI; the rendering parameters are automatically computed to guarantee the visibility of 3D features; and changes in 3D features can be tracked in temporal data under the constraint of consistent contextual information. We also present a method for the efficient computation of visibility histograms (VHs) using adaptive binning, which allows our optimal DVR to be automated and visualized in real-time. We evaluated our methods by producing visualizations for a variety of clinically relevant scenarios and imaging data sets. We also examined the computational performance of our methods for these scenarios