FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++ based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/.Comment: 12 pages, 4 figures, submitted to IEEE Acces

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering

H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images

Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumor segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumor segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933±0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872±0.092) and a low-resolution U-Net (0.874±0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative × 400 WSI took ~ 58 s, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk. Future work should involve more efficient patch generation and sampling, as well as improved clustering.publishedVersio

Undersøkelser av infeksjonskjeden for Streptococcus dysgalactiae-infeksjoner i norsk sauehold og melkekubesetninger : risikofaktorer, kilder og genomisk diversitet

Streptococcus dysgalactiae subspecies dysgalactiae (SDSD) is an important cause of outbreaks of infectious arthritis in lambs and mastitis in dairy cows in Norway. The infections compromise animal welfare, increase antimicrobial usage and reduce production. The Norwegian sheep and dairy industries state that streptococcal infections are one of the major challenges to production. The aim of this work was to increase our understanding of how SDSD spreads and causes disease in in sheep and cattle in Norway. We approached the aim through risk factor studies, exploration of bacterial sources, and genomic investigations of bacterial isolates from Norwegian sheep flocks and bovine dairy herds. Several risk factors related to modern management systems were identified. Larger sheep flocks with a lambing percentage above 200 had an increased risk of outbreaks of infectious arthritis in lambs. Intramammary infections caused by SDSD were more common in bovine dairy herds housed in freestalls compared to tiestalls. Certain types of flooring were a risk factor for SDSD infections in both sheep flocks (plastic mesh flooring) and bovine dairy herds (closed flooring). Analyses of samples collected from sheep flocks and bovine dairy herds revealed that SDSD is present in most of the visited sheep flocks and bovine dairy herds. SDSD is mainly associated with the host, but was also found in the environment. A greater proportion of environmental samples from bovine freestalls were SDSD positive compared to samples collected from sheep sheds during lambing. Wounds were particularly often colonized by SDSD in both animal species and may serve as a site of bacterial multiplication that increases the size of the bacterial reservoir. Prevention of wounds may therefore be an important measure to reduce SDSD infection pressure. Genomic investigations revealed a clonal population structure of the SDSD, and isolates were delineated according to host species. Highly similar strains were found in epidemiologically independent flocks and herds. We found no significant association between genotype and disease severity, defined as clinical mastitis in cows (compared to subclinical mastitis) and outbreaks of infectious arthritis in lambs. In conclusion, this study indicates that SDSD is an animal-adapted opportunist that has lived with the hosts over time. The work has contributed to our understanding of risk factors, sources, and transmission dynamics in modern management systems and also the genome and population structure of SDSD. It provides the basis for updated advice in the animal health services and will thus contribute to reducing SDSD-infections in Norwegian livestock, reducing animal suffering, and increasing productivity.Streptococcus dysgalactiae subspecies dysgalactiae (SDSD) er en viktig årsak til utbrudd av leddbetennelse hos lam og mastitt hos melkekyr i Norge. Infeksjonene fører til redusert dyrevelferd, økt forbruk av antibiotika og reduserer produksjonsutbyttet. Målet med dette arbeidet var å øke vår forståelse av hvordan SDSD-infeksjoner spres og gir sykdom hos sau og melkeku i Norge. For å oppnå målet gjennomførte vi risikofaktorstudier, undersøkte bakteriekilder og genomiske undersøkelser av bakterieisolater fra norske saueflokker og storfebesetninger. Flere risikofaktorer knyttet til moderne driftsformer ble identifisert. Større saueflokker med mer enn to lam per søye hadde økt risiko for utbrudd av smittsom leddbetennelse hos lam. Intramammare infeksjoner forårsaket av SDSD var mer vanlig i melkekubesetninger som ble holdt i løsdriftsfjøs sammenlignet med båsfjøs. Visse typer gulv var en risikofaktor for SDSD-infeksjoner i både saueflokker (plastrister) og melkekufjøs (tett gulv). Analyser av prøver samlet inn fra saueflokker og melkekubesetninger viste at SDSD er til stede i de fleste besøkte besetningene. Bakterien ser ut til å trives best på verten, men synes også å overleve en stund i miljøet. En større andel av miljøprøver fra melkekufjøs var SDSD-positive sammenlignet med prøver samlet inn fra sauefjøs under lamming. Sår ble spesielt ofte kolonisert av SDSD hos begge dyrearter, og kan fungere som et oppformeringssted for bakterien. Forebygging av sår kan derfor være et viktig tiltak for å redusere infeksjonspresset. Undersøkelser av bakteriens arvestoff viste at SDSD har en klonal populasjonsstruktur, hvor isolatene fra en vertsart var mer like innbyrdes. Svært like varianter ble funnet i epidemiologisk uavhengige flokker og besetninger. Resultatene indikerer at SDSD er en dyretilpasset opportunist som har levd med vertene over tid. Vi fant ingen signifikant sammenheng mellom genotype og sykdomsgrad, definert som klinisk mastitt hos kyr (sammenlignet med subklinisk mastitt) og utbrudd av leddbetennelse hos lam. Arbeidet gir grunnlag for oppdaterte råd for norske saue- og melkekuprodusenter, og vil bidra til å redusere SDSD-infeksjoner i norske husdyr, bedre dyrevelferd og øke produktiviteten.The PhD project was part of the research project “Increasing sustainability of Norwegian food production by tackling streptococcal infections in modern livestock systems”, coordinated by the Norwegian Veterinary Institute. It received financial contributions from the Norwegian Agricultural Agreement Research Fund and the Norwegian Research council (grant numbers 280364 and 288917), Animalia, and TINE SA

Prevalence of udder pathogens in milk samples from Norwegian dairy cows recorded in a national database in 2019 and 2020

Abstract Background Identification of aetiological agents of mastitis in dairy cattle is important for herd management of udder health. In Norway, results from mastitis diagnostics are systematically recorded in a central database, so that the dairy industry can follow trends in the recorded frequency of udder pathogens and antimicrobial resistance patterns at national level. However, bacteriological testing of milk samples is based on voluntary sampling, and data are therefore subject to some bias. The aim of this study was to examine the prevalence of udder pathogens in Norwegian dairy cows by analysing data from the national routine mastitis diagnostics and to explore how routines for sampling and diagnostic interpretations may affect the apparent prevalence of different bacterial pathogens. We also assessed associations between udder pathogen findings and the barn- and milking systems of the herds. Results The most frequently detected major udder pathogens among all milk samples submitted for bacterial culture (n = 36,431) were Staphylococcus aureus (24.5%), Streptococcus dysgalactiae (13.3%) and Streptococcus uberis (9.0%). In the subset of samples from clinical mastitis (n = 7598); Escherichia coli (14.5%) was the second most frequently detected pathogen following S. aureus (27.1%). Staphylococcus epidermidis (10.0%), Corynebacterium bovis (9.4%), and Staphylococcus chromogenes (6.0%) dominated among the minor udder pathogens. Non-aureus staphylococci as a group, identified in 39% of the sampling events, was the most frequently identified udder pathogen in Norway. By using different definitions of cow-level bacterial diagnoses, the distribution of minor udder pathogens changed. Several udder pathogens were associated with the barn- and milking system but the associations were reduced in strength when data were analysed from farms with a comparable herd size. S. aureus was associated with tiestall housing, E. coli and S. dysgalactiae were associated with freestall housing, and S. epidermidis was associated with automatic milking systems. Only 2.5% of the 10,675 tested S. aureus isolates were resistant to benzylpenicillin. Among the 2153 tested non-aureus staphylococci, altogether 34% were resistant to benzylpenicillin. Conclusions This study presents the recorded prevalence of udder pathogens in Norway over a two-year period and assesses the possible impact of the sampling strategies, diagnostic methods and diagnostic criteria utilized in Norway, as well as associations with different housing and milking systems. The national database with records of results from routine mastitis diagnostics in Norway provides valuable information about the aetiology of bovine mastitis at population level and can reveal shifts in the distribution and occurrence of udder pathogens

Flock-level risk factors for outbreaks of infectious arthritis in lambs, Norway 2018

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Longitudinal Study of the Bulk Tank Milk Microbiota Reveals Major Temporal Shifts in Composition

Introduction of microbial contaminations in the dairy value chain starts at the farm level and the initial microbial composition may severely affect the production of high-quality dairy products. Therefore, understanding the farm-to-farm variation and longitudinal shifts in the composition of the bulk tank milk microbiota is fundamental to increase the quality and reduce the spoilage and waste of milk and dairy products. In this study, we performed a double experiment to study long- and short-term longitudinal shifts in microbial composition using 16S rRNA gene amplicon sequencing. We analyzed milk from 37 farms, that had also been investigated two years earlier, to understand the stability and overall microbial changes over a longer time span. In addition, we sampled bulk tank milk from five farms every 1–2 weeks for up to 7 months to observe short-term changes in microbial composition. We demonstrated that a persistent and farm-specific microbiota is found in bulk tank milk and that changes in composition within the same farm are mostly driven by bacterial genera associated with mastitis (e.g., Staphylococcus and Streptococcus). On a long-term, we detected that major shift in milk microbiota were not correlated with farm settings, such as milking system, number of cows and quality of the milk but other factors, such as weather and feeding, may have had a greater impact on the main shifts in composition of the bulk tank milk microbiota. Our results provide new information regarding the ecology of raw milk microbiota at the farm level.</p

Whole genome sequencing reveals possible host species adaptation of Streptococcus dysgalactiae.

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Whole genome sequencing reveals possible host species adaptation of Streptococcus dysgalactiae.

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FastPathology: An open-source platform for deep learning-based research and decision support in digital pathology

Deep convolutional neural networks (CNNs) are the current state-of-the-art for digital analysis of histopathological images. The large size of whole-slide microscopy images (WSIs) requires advanced memory handling to read, display and process these images. There are several open-source platforms for working with WSIs, but few support deployment of CNN models. These applications use third-party solutions for inference, making them less user-friendly and unsuitable for high-performance image analysis. To make deployment of CNNs user-friendly and feasible on low-end machines, we have developed a new platform, FastPathology, using the FAST framework and C++. It minimizes memory usage for reading and processing WSIs, deployment of CNN models, and real-time interactive visualization of results. Runtime experiments were conducted on four different use cases, using different architectures, inference engines, hardware configurations and operating systems. Memory usage for reading, visualizing, zooming and panning a WSI were measured, using FastPathology and three existing platforms. FastPathology performed similarly in terms of memory to the other C++-based application, while using considerably less than the two Java-based platforms. The choice of neural network model, inference engine, hardware and processors influenced runtime considerably. Thus, FastPathology includes all steps needed for efficient visualization and processing of WSIs in a single application, including inference of CNNs with real-time display of the results. Source code, binary releases, video demonstrations and test data can be found online on GitHub at https://github.com/SINTEFMedtek/FAST-Pathology/