Histology image registration involves aligning microscopy images for various purposes, including creating 3D reconstructions from 2D, combining data from slices with different stain samples, and from multimodal registration. However, this process poses several challenges, including high resolution, non-linear elastic deformation, occlusions, missing sections, non-rigid deformation, contrast differences, and differences in appearance and local structure. Multimodal image registration is particularly challenging because different modalities may have different characteristics and require specific optimization algorithms. To address this issue, it is important to develop software that allows users to test different image registration algorithms and combine annotations made on them, in order to leverage the benefits of multiple modalities. To address this challenge, we developed a cloud-based Multimodal Image Registration system that enables developers and researchers to visually test the outcomes of various image registration algorithms. The system includes a project manager, an algorithm manager, and an image visualization system. The system was developed using the framework Django, JavaScript, and multiple libraries that facilitate the management and annotation of very high-resolution images. To demonstrate the effectiveness and flexibility of our system, we tested it using two different algorithms, SIFT and ORB, on nonlinear multimodal and brightfield images using the Hematoxylin and Eosin staining methods. The results show the system's ability to handle challenging image registration tasks while providing visualization tools to improve user experience.

In the last decade, digital technologies have been transforming clinical pathology, dematerializing the processes, improving the workflow, and promoting the development of decision support tools. The glass slides can be digitized with high-quality scanners and sent to network archives, opening doors to data sharing and universal access. Despite these advancements, however, the adoption of digital pathology solutions in production environments has been slowed down by the lack of standardization in the available software. Among the improved visualization features in digital pathology is the ability to capture a series of images at the same spatial location with varying focal planes, also known as Z-stack. This article proposes a full-stack, standard solution that efficiently supports the visualization of these 4D images. It is a Cloud-based architecture that supports Z-stack image management and visualization. The solution is made vendor-neutral by providing a pipeline for the integration of different image vendors.

Recent advancements in the acquisition of digital imaging modalities with high-throughput technologies, such as confocal laser scanner microscopy (CLSM) and focused-ion beam scanning electron microscopy (FIB-SEM), are providing researchers with unprecedented opportunities to collect massive amounts of multidimensional datasets. This data can be used to visualize the internal structure of tiny particles (mostly cells and organisms) or to develop analytic algorithms. Visualizing newly obtained multidimensional microscope imaging data is beyond the capabilities of traditional 3D visualization packages, as it carries much information in the form of additional dimensions. Typically, these extra dimensions correspond to space, time, and channels, which has driven the development of new visualization applications. In this article, we describe the design and implementation of an interactive web-based multidimensional 3D visualization tool for CLSM and FIB-SEM microscope imaging modalities. The proposed 3D visualization application accepts DICOM files as input and provides a variety of visualization choices ranging from 3D volume/surface rendering to multiplanar reconstruction approaches. The solution performance was tested by uploading and rendering microscopy images of distinct modalities.

Because of its high-resolution visibility across bone tissue, digital whole slide imaging (WSI) in pathology has become increasingly popular in biomedicine for diagnostic, educational, and research purposes. The technology, however, is not yet sufficiently mature to support remote clinical practice in pathology. Several technical challenges have hampered the gradual rollout of telematics platforms. Most notably, there is a lack of system interoperability because scanner manufacturers have yet to adopt a standard for data format and communications processes, specifically the DICOM standard, which already supports WSI. At the moment, it is uncommon to find an institutional repository capable of acquiring, storing, and visualizing samples scanned by different vendors’ equipment. Although some vendors offer DICOM interfaces, the vast majority still use proprietary solutions. This article proposes a framework for multi-vendor data integration through the dicomization of proprietary images, including metadata, and optimization of the visualization process, with a focus on the stacked gigapixel WSI produced by new generation scanners. These images are difficult to preprocess and visualize in most environments due to their size, which is typically in the hundreds of Gigabytes range, and there are no reliable packages that can load, preprocess, and visualize these stacked WSI on a single platform. In this work, we created a simple automated pipeline that can read the majority of proprietary WSI images and focal planes, convert them to DICOM for preprocessing and visualization, and make them compatible with modern Web PACS platforms. The solution was tested with eight distinct proprietary files, two of which were stacked images, from acquisition to Web visualization, demonstrating good performance and efficient use of computational resources.

In recent years, the quality of fluorescence microscopic imaging produced by a laser scanning microscope (LSM) system has been increased through the use of optical imaging techniques being used for small biological or nonbiological particles like instance, bacteria or cells in tissue samples. Currently, multiple manufactures provide LSM equipment able to capture single-layer or stacked images. However, the manufactures still using distinct data formats including proprietary ones, limiting the interoperability with third systems. In the clinical environment, the scanners need to be integrated with a vendor-neutral archive, storing images in a standard format and interface, making them accessible to healthcare professionals regardless of what proprietary system created it. Having distinct software solutions to manage the data is tiresome and time-consuming. This article proposes a normalization pipeline that can convert distinct vendor formats in a standard DICOM structure including pixel data and metadata. After conversion, the images are sent to a DICOM-compliant repository being able to be consumed in the network using normalized communication and visualization processes. The proposed solution is reliable and uses efficiently the less memory, a critical issue since the resolution of pathology whole-slide images can reach several gigapixels.

In the nineties, the adoption of the DICOM standard format in radiology departments brought numerous advantages to clinical practice. The setup of PACS with standard communication processes and data formats allowed the creation of central repositories, fast retrieval of images, visualization of images acquired with several modalities, and simultaneous access at distributed places. Nowadays, microscopy imagining faces the same normalization challenge with the proliferation of equipment that stores data in a proprietary format and provides dedicated visualization software. This reality severely limits the implementation of vendor-neutral archives with common visualization processes, conditioning the research work and its integration in clinical environments. This paper proposed a pipeline for the integration of multiple microscopy imaging modalities into the PACS-DICOM universe, including the numerous metadata elements. A proof-of-concept system was developed, for validation purposes, and integrated with the Dicoogle open-source PACS, providing image storage, metadata indexing and visualization.