Digital Atlas Research Articles

The ADDIE Method in Digital Atlas-Based Learning Media on Practicum Learning Outcomes in Histology of Medical Students

Histology is one of the basic sciences that is difficult to learn, as evidenced by low practicum scores. The purpose of this research is to develop histology digital atlas-based learning media for medical students at Universitas Negeri Gorontalo. This digital atlas-based media aims to enhance student learning outcomes. The ADDIE method was used to develop the learning media. The results show an improvement in the learning outcomes of medical students in the histology practicum. It implies to use ADDIE for everyone who wants to develop learning media. It contributes to all teachers to use ADDIE method in Digital Atlas based learning media on practicum learning outcome.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation.

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo. In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain, a next-generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ~10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain. Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ~4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 μm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

The Human Cerebellum: A Digital Anatomical Atlas at the Level of Individual Folia.

Scientific interest in the cerebellum has surged in the last few decades with an emerging consensus on a multifaceted functionality and intricate, but not yet fully understood, functional topography over the cerebellar cortex. To further refine this structure-function relationship and quantify its inter-subject variability, a high-resolution digital anatomical atlas is fundamental. Using a combination of manual labeling and image processing, we turned a recently published reconstruction of the human cerebellum, the first such reconstruction fine enough to resolve the individual folia, into a digital atlas with both surface and volumetric representations. Its unprecedented granularity (0.16 mm) and detailed expert labeling make the atlas usable as an anatomical ground truth, enabling new ways of analyzing and visualizing cerebellar data through its digital format.

Towards a chronological framework for early Irish carved stone: reassessing the evidence of Latinate inscriptions

The Digital Atlas of Early Irish Carved Stone (DAEICS) seeks to record and map key traits of carved stone monuments of late prehistoric and early medieval date, providing fresh perspectives on stone-carving over the course of a millennium. First, however, the question of chronology must be addressed, so here we reassess the potential of Latinate inscriptions as a dating tool. Rather than a comprehensive treatment of palaeography and language, we focus, first, on the small number of monuments that commemorate historically identifiable individuals and, second, on the chronological implications of the use or non-use of formulae, proposing new termini post quem for different variations. We also show that the appearance of Gilla- compound personal names on inscriptions allows the monuments in question to be dated to after 950 CE. The implications of the inscriptions for the date-ranges of some specific cross-forms are explored, above all with respect to the singularly large collection of carved stone monuments at Clonmacnoise, which we argue was the only site in Ireland where stone-carving was sustained over several centuries. Using the dateable inscriptions, we demonstrate that there was a general increase in the average size of recumbent grave-markers between 600 and 1200 CE, a trend indicative of changing strategies of commemoration that has important implications for the chronology of these monuments, especially those that cannot readily be dated on other criteria. This refined chronology will facilitate further research on the key questions raised by these monuments, including patterns of patronage and resource investment, the varied identities of ecclesiastical communities, and networks of carvers and churches.

Testing the Digital Atlas of Ancient Rare Diseases (DAARD) using a new case of Legg-Calvé-Perthes disease from Early Byzantine (500–700 CE) Olympia, Greece

ObjectiveThe first case of Legg-Calvé-Perthes disease (LCPD) in Greece is presented. LCPD, a rare disease, is discussed using the Digital Atlas of Ancient Rare Diseases (DAARD), which tests the benefits of the database for diagnosing and contextualizing the new case with 42 archaeological cases of LCPD recorded in the DAARD. MaterialsA 30–40-year-old, probable male individual was found at the archaeological site of Olympia, Greece, dating to 500–700 CE. MethodsBiological sex, age-at-death and pathological changes were investigated using macroscopic and osteometric methods. The DAARD provided the typical characteristics of LCPD. ResultsPathological changes in both hip joints without any other related changes in the skeleton corresponded to the skeletal features of LCPD. The DAARD produced 42 cases of LCPD, most of which from Europe, with a preference for male sex and unilateral involvement of the hip joint. ConclusionsThe DAARD aids in diagnosing rare diseases and interpreting new cases in the context of already known studies. SignificanceThis study shows that the DAARD has the potential to help researchers move beyond the level of single case studies and create a broader picture of the history of rare diseases. LimitationsThis paper focuses on the benefits of the DAARD in relation to LCPD but not all rare diseases have been included in the database. Suggestions for further researchMore rare diseases from archaeological contexts should be added to the DAARD to create a base for the interpretation of their history and expand our understanding of rare diseases in the past.

The Digital Atlas of Ancient Rare Diseases (DAARD) and its relevance for current research

BackgroundThe history of rare diseases is largely unknown. Research on this topic has focused on individual cases of prominent (historical) individuals and artistic (e.g., iconographic) representations. Medical collections include large numbers of specimens that exhibit signs of rare diseases, but most of them date to relatively recent periods. However, cases of rare diseases detected in mummies and skeletal remains derived from archaeological excavations have also been recorded. Nevertheless, this direct evidence from historical and archaeological contexts is mainly absent from academic discourse and generally not consulted in medical research on rare diseases.ResultsThis desideratum is addressed by the Digital Atlas of Ancient Rare Diseases (DAARD: https://daard.dainst.org), which is an open access/open data database and web-based mapping tool that collects evidence of different rare diseases found in skeletons and mummies globally and throughout all historic and prehistoric time periods. This easily searchable database allows queries by diagnosis, the preservation level of human remains, research methodology, place of curation and publications. In this manuscript, the design and functionality of the DAARD are illustrated using examples of achondroplasia and other types of stunted growth.ConclusionsAs an open, collaborative repository for collecting, mapping and querying well-structured medical data on individuals from ancient times, the DAARD opens new avenues of research. Over time, the number of rare diseases will increase through the addition of new cases from varied backgrounds such as museum collections and archaeological excavations. Depending on the research question, phenotypic or genetic information can be retrieved, as well as information on the general occurrence of a rare disease in selected space–time intervals. Furthermore, for individuals diagnosed with a rare disease, this approach can help them to build identity and reveal an aspect of their condition they might not have been aware of. Thus, the DAARD contributes to the understanding of rare diseases from a long-term perspective and adds to the latest medical research.

Radiological Assessment of Hand and Wrist Bones in Malnourished Children

Abstract Background Protein energy malnutrition (PEM) is still an important health problem that is responsible for childhood morbidity and mortality leading to permanent impairment of physical and mental growth. Objective This study is designed to assess the appearance of ossification centers of lower end of radius and ulna, carpals, metacarpals and phalanges in normal children and malnourished children then comparing between them, to detect early changes in bone growth in malnourished children. Subjects and Methods This case-control cross-sectional study included 45 malnourished children and 45 age and sex matched controls whose ages ranged between 6 months and 5 years. The patients were recruited from Pediatric Nutrition Clinic of Ain Shams University. All children (malnourished and control) were examined for anthropometric parameters (weight, height, body mass index (BMI) and mid upper arm circumference (MUAC)), investigated for serum calcium, phosphorus, alkaline phosphates and had a plain X-ray done on left hand and wrist (posteroanterior (P-A) view), the X-ray was analyzed by using A Digital Atlas of Skeletal Maturity. Results The plain X-ray hand and wrist showed delayed appearance of ossification centers in malnourished infants and children (97.8% had delayed bone age) when compared to controls (25.5% had delayed bone age) which was statistically highly significant with P-value < 0.01. Signs of vitamin D deficiency rickets together with serum calcium, phosphorus and alkaline phosphatase were statistically significantly lower in the cases when compared to the controls with P-value < 0.01. The statistical measurements of mean ± SD regarding weight of case group and control group was (9.05 ± 2.48, 13.54 ± 3.81) with P-value < 0.01 which is highly significant, regarding length or height was (79.51 ± 12.78, 88.94 ± 15.02) with P-value < 0.01 which is highly significant, regarding MUAC was (11.38 ± 1.91, 16.02 ± 1.26) with P-value < 0.01 which is highly significant. Conclusion X-ray hand and wrist for malnourished infants and children showed delayed appearance of ossification centers when compared to matched controls.

Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma.

We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment. We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment. We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

A Two‐stage Bayesian Small Area Estimation Approach for Proportions

SummaryWith the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two‐stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.

Distribution of Silent Cerebral Infarcts in Adults With Sickle Cell Disease.

Previously we demonstrated that 90% of infarcts in children with sickle cell anemia occur in the border zone regions of cerebral blood flow (CBF). We tested the hypothesis that adults with sickle cell disease (SCD) have silent cerebral infarcts (SCIs) in the border zone regions, with a secondary hypothesis that older age and traditional stroke risk factors would be associated with infarct occurrence in regions outside the border zones. Adults with SCD 18-50 years of age were enrolled in a cross-sectional study at 2 centers and completed a 3T brain MRI. Participants with a history of overt stroke were excluded. Infarct masks were manually delineated on T2-fluid-attenuated inversion-recovery MRI and registered to the Montreal Neurological Institute 152 brain atlas to generate an infarct heatmap. Border zone regions between anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA) were quantified using the Digital 3D Brain MRI Arterial Territories Atlas, and logistic regression was applied to identify relationships between infarct distribution, demographics, and stroke risk factors. Of 113 participants with SCD (median age 26.1 years, interquartile range [IQR] 21.6-31.4 years, 51% male), 56 (49.6%) had SCIs. Participants had a median of 5.5 infarcts (IQR 3.2-13.8). Analysis of infarct distribution showed that 350 of 644 infarcts (54.3%) were in 4 border zones of CBF and 294 (45.6%) were in non-border zone territories. More than 90% of infarcts were in 3 regions: the non-border zone ACA and MCA territories and the ACA-MCA border zone. Logistic regression showed that older participants have an increased chance of infarcts in the MCA territory (odds ratio [OR] 1.08; 95% CI 1.03-1.13; p = 0.001) and a decreased chance of infarcts in the ACA-MCA border zone (OR 0.94; 95% CI 0.90-0.97; p < 0.001). The presence of at least 1 stroke risk factor did not predict SCI location in any model. When compared with children with SCD, in adults with SCD, older age is associated with expanded zones of tissue infarction that stretch beyond the traditional border zones of CBF, with more than 45% of infarcts in non-border zone regions.

NEUROANATOMY, DIGITAL ATLASES, AND TEMPLATES OF BRAINS OF DOMESTIC ANIMALS (CAT, DOG, PIG, HORSE and SHEEP) USING MAGNETIC RESONANCE IMAGING

Over the last two decades, neuroscience has witnessed an explosion in the utilization of non-invasive imaging methods (particularly MRI) that are used to investigate to study the brain. Providing accurate and detailed imaging, MRI has a significant impact on figuring out the anatomy and functioning of the brain. In recent years, researchers studying on veterinary science have seen MRI as an indispensable tool themselves. It is essential to understand the anatomy of the normal brain in order to explain many of the pathological processes. This review focused on neuroanatomical studies, atlases and templates generated from the brains of domestic animals (cat, dog, pig, horse, donkey, cattle, sheep, goat, camel) using MRI from the 1980s to the present. Its data were summarized under three main parts. Firstly, the cross-sectional anatomy of the brain created using MRI was examined. Afterward, digital atlases and templates, which have had an essential place in modern neuroimaging analysis (such as registration, segmentation and three-dimensional reconstruction) in recent years, were summarized. Finally, in vivo or ex vivo studies in which crucial white matter tracts in the brain are three-dimensionally modeled with DTI (Diffusion Tensor Imaging) in domestic mammals were reviewed. Several studies examining the neocortex by DTI were also included in the review in this section. There were also neuroanatomy studies conducted with MRI in several specific species in this review. In conclusion, this review focused on the importance of MRI in neuroimaging of the brain in domestic animals.

The digital climate atlas of the Canary Islands: A tool to improve knowledge of climate and temperature and precipitation trends in the Atlantic islands

This paper presents the Interactive Digital Climate Atlas of the Canary Islands, a web portal based on geographic information systems for the dissemination of climate information. It provides citizens and public and private administrations with high spatial resolution maps (100 m) of different climate variables such as temperature, rainfall, relative humidity, cloudiness frequency and wind velocity. Also available are Köppen climate classification maps and decadal trends of temperature and precipitation in a context of climate change. This spatial resolution is suitable for the detailed climatic description of small and orographically complex islands. For this purpose, climate series from databases of different public institutions as well as data from remote sensing and reanalysis systems were employed. A statistical process involving data filtering and the detection and correction of inhomogeneous segments was carried out as a preparatory step. Then, a spatial interpolation model was developed using a multiple linear regression method for the generation of the high spatial resolution climate cartography. The results are annual and monthly maps of the climate variables in Canary Islands at 100 m spatial resolution and with different time periods depending on the availability of data on the different variables, but in most cases between 1991 and 2020. Following the European Directive INSPIRE (Directive 2007/2/CE, Infrastructure for Spatial Information in Europe), both map visualisation and downloading in GeoTIFF and NetCDF format are permitted, as well as consultation of the data and metadata of the climatic series used.

Evaluating the Robustness of a Deep Learning Bone Age Algorithm to Clinical Image Variation Using Computational Stress Testing.

Purpose To evaluate the robustness of an award-winning bone age deep learning (DL) model to extensive variations in image appearance. Materials and Methods In December 2021, the DL bone age model that won the 2017 RSNA Pediatric Bone Age Challenge was retrospectively evaluated using the RSNA validation set (1425 pediatric hand radiographs; internal test set in this study) and the Digital Hand Atlas (DHA) (1202 pediatric hand radiographs; external test set). Each test image underwent seven types of transformations (rotations, flips, brightness, contrast, inversion, laterality marker, and resolution) to represent a range of image appearances, many of which simulate real-world variations. Computational "stress tests" were performed by comparing the model's predictions on baseline and transformed images. Mean absolute differences (MADs) of predicted bone ages compared with radiologist-determined ground truth on baseline versus transformed images were compared using Wilcoxon signed rank tests. The proportion of clinically significant errors (CSEs) was compared using McNemar tests. Results There was no evidence of a difference in MAD of the model on the two baseline test sets (RSNA = 6.8 months, DHA = 6.9 months; P = .05), indicating good model generalization to external data. Except for the RSNA dataset images with an appended radiologic laterality marker (P = .86), there were significant differences in MAD for both the DHA and RSNA datasets among other transformation groups (rotations, flips, brightness, contrast, inversion, and resolution). There were significant differences in proportion of CSEs for 57% of the image transformations (19 of 33) performed on the DHA dataset. Conclusion Although an award-winning pediatric bone age DL model generalized well to curated external images, it had inconsistent predictions on images that had undergone simple transformations reflective of several real-world variations in image appearance. Keywords: Pediatrics, Hand, Convolutional Neural Network, Radiography Supplemental material is available for this article. © RSNA, 2024 See also commentary by Faghani and Erickson in this issue.

The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases.

Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize, integrate, and analyze data based on anatomical locations. While structured metadata allow faceted data queries, different types of data are not easily represented in a standardized and machine-readable way that allow comparison, analysis, and queries related to anatomical relevance. To this end, three-dimensional (3D) digital brain atlases provide frameworks in which disparate multimodal and multilevel neuroscience data can be spatially represented. We propose to represent the locations of different neuroscience data as geometric objects in 3D brain atlases. Such geometric objects can be specified in a standardized file format and stored as location metadata for use with different computational tools. We here present the Locare workflow developed for defining the anatomical location of data elements from rodent brains as geometric objects. We demonstrate how the workflow can be used to define geometric objects representing multimodal and multilevel experimental neuroscience in rat or mouse brain atlases. We further propose a collection of JSON schemas (LocareJSON) for specifying geometric objects by atlas coordinates, suitable as a starting point for co-visualization of different data in an anatomical context and for enabling spatial data queries.

The EuroWeb Textile and Clothing Terminology Network and the Digital Atlas of European Textile Heritage: Some Reflections and Results

One of the research areas of the EuroWeb project during the four years of the COST Action (November 2020 – October 2024) is the comparative study of textile and clothing terminologies in European languages across time. Inside the EuroWeb network, the research group on Textile and clothing terminologies has three topics of particular interest: 1. the specificities of these terminologies, and the strategies for naming textiles and garments; 2. the impact of European geography on textile and clothing terminologies, especially visible through textile terms formed after a toponym or through the circulation of loanwords; 3. the influence of textile and clothing terminologies on texts, languages, and literature. Although the geographical scope of the project is focused on Europe, it also includes neighboring areas. The terminology research group counted more than 120 people, specialists in the study of modern or ancient textile terminologies who have various disciplinary profiles: Historians, epigraphists, linguists and terminologists, archaeologists, art historians, and museums curators. The aim of the EuroWeb COST Action is to bring together these experts in different fields related to textiles studies through joint meetings, scientific conferences, and training schools, but also through collaborative research and brainstorming around the construction of common tools. Examples include a terminological vocabulary of the basic terms of our field of study, or an online platform to share knowledge on the terminology of textiles and clothing. In this article, we want to focus on the work carried out during the first years of the EuroWeb project (November 2020 – October 2024) by the terminology research group, starting with a focus on the collaboration with the team involved in the creation of the Digital Atlas of European Textile Heritage.

Towards a chronological framework for early Irish carved stone: Reassessing the evidence of Latinate inscriptions

Abstract: The Digital Atlas of Early Irish Carved Stone (DAEICS) seeks to record and map key traits of carved stone monuments of late prehistoric and early medieval date, providing fresh perspectives on stone-carving over the course of a millennium. First, however, the question of chronology must be addressed, so here we reassess the potential of Latinate inscriptions as a dating tool. Rather than a comprehensive treatment of palaeography and language, we focus, first, on the small number of monuments that commemorate historically identifiable individuals and, second, on the chronological implications of the use or non-use of formulae, proposing new termini post quem for different variations. We also show that the appearance of Gilla- compound personal names on inscriptions allows the monuments in question to be dated to after 950 CE. The implications of the inscriptions for the date-ranges of some specific cross-forms are explored, above all with respect to the singularly large collection of carved stone monuments at Clonmacnoise, which we argue was the only site in Ireland where stone-carving was sustained over several centuries. Using the dateable inscriptions, we demonstrate that there was a general increase in the average size of recumbent grave-markers between 600 and 1200 CE, a trend indicative of changing strategies of commemoration that has important implications for the chronology of these monuments, especially those that cannot readily be dated on other criteria. This refined chronology will facilitate further research on the key questions raised by these monuments, including patterns of patronage and resource investment, the varied identities of ecclesiastical communities, and networks of carvers and churches.

Development and Validation of a Digital Photographic Atlas of Argentine Foods

Introduction. Before the Second National Health and Nutrition Survey in Argentina, it was necessary to create a digital visual tool to help participants in the quantification of intake. This study describes the development of a Digital Photographic Atlas of Argentinean Foods (AFDAA) and evaluates its accuracy in visually estimating the amounts of foods consumed in Argentina. Methods. A total of 292 photographs of food/dishes were taken in standardized conditions and classified into 103 series according to food group. Thirty series were selected for validation. Adults ≥ 18 years of age were invited to participate in three validation sessions that were conducted at “blinded” between 2016 and 2018. During each session, the participant´s ability to visually relate a real amount of food presented on a plate to an amount depicted in a photograph series was assessed. The difference between the participant’s perception of the weight of foods/dishes in photographs and the real weight was expressed as a percentage. The average percentage difference was estimated, and 95% confidence intervals were used. When ≥ 50% of the differences were outside the ±30% range, the set of pictures was removed from the final version. Results. This free-to-use digital Atlas is a valuable tool that can be employed in future dietary surveys to quantify the consumption of foods similar to those depicted in the images. Conclusion. The degree of BMI tends to increase the level of sleepiness. Funding: This research received financial support from UNICEF and the Universidad Nacional de La Matanza (Grant C2SAL012).

A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital “atlas” as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through “majority vote” and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.

Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration

Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.

Zebrafish brain atlases: a collective effort for a tiny vertebrate brain.

In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.