In recent years, WebAssembly (Wasm) has emerged as a widely-supported technology that offers high performance, compact binary size, support for multiple languages, hardware independence, security, and universal platform support, enabling developers to bring near-native speeds and portability to applications for the web and beyond. ITK-Wasm brings WebAssembly’s capabilities to scientific computing by combining the Insight Toolkit (ITK) and WebAssembly to enable high-performance spatial analysis across programming languages and hardware architectures.In the scientific Python ecosystem, ITK-Wasm packages work in a web browser via Pyodide but also in system-level environments through the WebAssembly System Interface (WASI). ITK-Wasm bridges WebAssembly with scientific Python through simple, fundamental Python and NumPy-based data structures and Pythonic function interfaces. These interfaces can be accelerated through graphics processing units (GPU) or neural processing unit (NPU) implementations when available.Beyond Python, ITK-Wasm’s integration of the WebAssembly Component Model launches scientific computing into a new world of interoperability, enabling the creation of accessible and sustainable multi-language projects that are easily distributed anywhere.

Discover how scikit-build-core revolutionizes Python extension building with its seamless integration of CMake and Python packaging standards. Learn about its enhanced features for cross-compilation, multi-platform support, and simplified configuration, which enable writing binary extensions with pybind11, Nanobind, Fortran, Cython, C++, and more. Dive into the transition from the classic scikit-build to the robust scikit-build-core and explore its potential to streamline package distribution across various environments.

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A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Image registration plays a vital role in understanding changes that occur in 2D and 3D scientific imaging datasets. Registration involves finding a spatial transformation that aligns one image to another by optimizing relevant image similarity metrics. In this paper, we introduce itk-elastix, a user-friendly Python wrapping of the mature elastix registration toolbox. The open-source tool supports rigid, affine, and B-spline deformable registration, making it versatile for various imaging datasets. By utilizing the modular design of itk-elastix, users can efficiently configure and compare different registration methods, and embed these in image analysis workflows.

The diversity and utility of cinematic volume rendering (CVR) for medical image visualisation have grown rapidly in recent years. At the same time, volume rendering on augmented and virtual reality systems is attracting greater interest with the advance of the WebXR standard. This paper introduces CVR extensions to the open-source visualisation toolkit (vtk.js) that supports WebXR. This paper also summarises two studies that were conducted to evaluate the speed and quality of various CVR techniques on a variety of medical data. This work is intended to provide the first open-source solution for CVR that can be used for in-browser rendering as well as for WebXR research and applications. This paper aims to help medical imaging researchers and developers make more informed decision when selecting CVR algorithms for their applications. Our software and this paper also provide a foundation for new research and product development at the intersection of medical imaging, web visualisation, XR and CVR.

Epidemiological studies indicate that microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. An undetected crack will continue to progress, often with significant pain, until the tooth is lost. Previous attempts to utilize cone beam computed tomography (CBCT) for detecting cracks in teeth had very limited success. We propose a model that detects cracked teeth in high resolution (hr) CBCT scans by combining signal enhancement with a deep CNN-based crack detection model. We perform experiments on a dataset of 45 ex-vivo human teeth with 31 cracked and 14 controls. We demonstrate that a model that combines classical wavelet-based features with a deep 3D CNN model can improve fractured tooth detection accuracy in both micro-Computed Tomography (ground truth) and hr-CBCT scans. The CNN model is trained to predict a probability map showing the most likely fractured regions. Based on this fracture probability map we detect the presence of fracture and are able to differentiate a fractured tooth from a control tooth. We compare these results to a 2D CNN-based approach and we show that our approach provides superior detection results. We also show that the proposed solution is able to outperform oral and maxillofacial radiologists in detecting fractures from the hr-CBCT scans. Early detection of cracks will lead to the design of more appropriate treatments and longer tooth retention.

We demonstrate the application of the Thin Shell Demons (TSD) surface registration algorithm in registering the dental scans obtained from intra-oral scanners (IOS) and Cone Beam Computed Tomography (CBCT) in a semi-automatic manner. The reconstructed dentition obtained from CBCT lacks the accuracy for diagnosis and appliance fabrication that IOS provides. Current methods to register IOS to CBCT typically use Iterative Closest Point (ICP) but suffer from a lack of precision and accuracy. TSD registration has previously been shown to produce superior registration results in presence of missing patches and holes. In this work, for the first time we share its application in dental scan registration. We perform experiments on dental surface meshes obtained from CBCT and IOS for two patients who have undergone orthognathic surgery. Our method first registers the IOS mesh with the CBCT mesh using ICP. To obtain tight alignment of the tooth surface, we perform a refinement using the TSD registration. We quantify the improvement in registration by measuring the distance between the closest points present on the surface of six teeth in the IOS and CBCT meshes. Compared to using only ICP registration, TSD decreases the mean surface distance between patches and significantly improves the alignment. We also share qualitative results that clearly demonstrate the improvement due to TSD. For wide adoption and ease of access, we also share a publicly available Thin Shell Demons implementation in C++ under the open-source image processing library Insight Toolkit (ITK). Python wrapping of the code is also made available. The code is available at the url: https://github.com/InsightSoftwareConsortium/ITKThinShellDemons.

Shear wave elastography (SWE) is an ultrasound‐based stiffness quantification technology that is used for noninvasive liver fibrosis assessment. However, despite widescale clinical adoption, SWE is largely unused by preclinical researchers and drug developers for studies of liver disease progression in small animal models due to significant experimental, technical, and reproducibility challenges. Therefore, the aim of this work was to develop a tool designed specifically for assessing liver stiffness and echogenicity in small animals to better enable longitudinal preclinical studies. A high‐frequency linear array transducer (12‐24 MHz) was integrated into a robotic small animal ultrasound system (Vega; SonoVol, Inc., Durham, NC) to perform liver stiffness and echogenicity measurements in three dimensions. The instrument was validated with tissue‐mimicking phantoms and a mouse model of nonalcoholic steatohepatitis. Female C57BL/6J mice (n = 40) were placed on choline‐deficient, L‐amino acid‐defined, high‐fat diet and imaged longitudinally for 15 weeks. A subset was sacrificed after each imaging timepoint (n = 5) for histological validation, and analyses of receiver operating characteristic (ROC) curves were performed. Results demonstrated that robotic measurements of echogenicity and stiffness were most strongly correlated with macrovesicular steatosis (R 2 = 0.891) and fibrosis (R 2 = 0.839), respectively. For diagnostic classification of fibrosis (Ishak score), areas under ROC (AUROCs) curves were 0.969 for ≥Ishak1, 0.984 for ≥Ishak2, 0.980 for ≥Ishak3, and 0.969 for ≥Ishak4. For classification of macrovesicular steatosis (S‐score), AUROCs were 1.00 for ≥S2 and 0.997 for ≥S3. Average scanning and analysis time was <5 minutes/liver. Conclusion: Robotic SWE in small animals is feasible and sensitive to small changes in liver disease state, facilitating in vivo staging of rodent liver disease with minimal sonographic expertise.

Stitching partially overlapping image tiles into a montage is a common requirement for materials microscopy. We developed ITKMontage, a new module for the open-source Insight Toolkit (ITK), capable of robustly and quickly generating extremely large, high-bit-depth montages within the memory constraints of standard workstations. The phase correlation method is at the core of our pairwise tile registration algorithm. Precise alignment of tiles acquired in typical raster patterns is enhanced with sub-pixel fitting and cropping to overlap. Fast Fourier transform (FFT)-based correlation is improved through a variety of padding methods and an added adjustable bias toward an expected translation. To arrange the tiles into the overall montage, we use global least squares minimization with outlier detection and removal. To blend tiles smoothly, each tile's contribution is weighted by the distance from the tile's edge. Results are demonstrated on several material science data sets and 3D images. Results compare favorably to ImageJ/Fiji plugin Image Stitching. The tool is integrated into the DREAM.3D software suite for multidimensional, multimodal microstructural data.

Shape analysis is an important and powerful tool in a wide variety of medical applications. Many shape analysis techniques require shape representations which are in correspondence. Unfortunately, popular techniques for generating shape representations do not handle objects with complex geometry or topology well, and those that do are not typically readily available for non-expert users. We describe a method for generating correspondences across a population of objects using a given template. We also describe its implementation and distribution via SlicerSALT, an open-source platform for making powerful shape analysis techniques more widely available and usable. Finally, we show results of this implementation on mouse femur data.

Microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. If they are not detected early, they continue to progress until the tooth is lost. Cone beam computed tomography (CBCT) has been used to detect microfractures, but has had very limited success. We propose an algorithm to detect cracked teeth that pairs high resolution (hr) CBCT scans with advanced image analysis and machine learning. First, microfractures were simulated in extracted human teeth (n=22). hr-CBCT and microCT scans of the fractured and control teeth (n=14) were obtained. Wavelet pyramid construction was used to generate a phase image of the Fourier transformed scan which were fed to a U-Net deep learning architecture that localizes the orientation and extent of the crack which yields slice-wise probability maps that indicate the presence of microfractures. We then examine the ratio of high-probability voxels to total tooth volume to determine the likelihood of cracks per tooth. In microCT and hr-CBCT scans, fractured teeth have higher numbers of such voxels compared to control teeth. The proposed analytical framework provides a novel way to quantify the structural breakdown of teeth, that was not possible before. Future work will expand our machine learning framework to 3D volumes, improve our feature extraction in hr-CBCT and clinically validate this model. Early detection of microfractures will lead to more appropriate treatment and longer tooth retention.

Abstract Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative measurements from these images is challenging for researchers who are not image analysis specialists. In this article, we describe HistomicsML2, a software tool for learn-by-example training of machine learning classifiers for histologic patterns in whole-slide images. This tool improves training efficiency and classifier performance by guiding users to the most informative training examples for labeling and can be used to develop classifiers for prospective application or as a rapid annotation tool that is adaptable to different cancer types. HistomicsML2 runs as a containerized server application that provides web-based user interfaces for classifier training, validation, exporting inference results, and collaborative review, and that can be deployed on GPU servers or cloud platforms. We demonstrate the utility of this tool by using it to classify tumor-infiltrating lymphocytes in breast carcinoma and cutaneous melanoma. Significance: An interactive machine learning tool for analyzing digital pathology images enables cancer researchers to apply this tool to measure histologic patterns for clinical and basic science studies.