Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing

Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large). As the baseline for our workflow testing, we use a Unet++ model trained on histological images of canine lymphoma with individually labeled nuclei. As an alternative to the Unet++, we also used a publicly available pre-trained and unmodified instance segmentation model called Stardist to demonstrate that our modular classification workflow can be combined with different types of segmentation models if they can provide proper nuclei segmentation. Subsequent to nuclear segmentation, we optimize algorithmic parameters for accurate classification of nuclear size using a newly derived reference size and final image classification based on a pathologists-derived ground truth. Our image classification module achieves a classification accuracy of up to 92% on canine lymphoma data. Compared to the accuracy ranging from 66.67 to 84% achieved using measurements provided by three individual pathologists, our algorithm provides a higher accuracy level and reproducible results. Our workflow also demonstrates a high transferability to feline lymphoma, as shown by its accuracy of up to 84.21%, even though our workflow was not optimized for feline lymphoma images. By determining the nuclear size distribution in tumor areas, our workflow can assist pathologists in subtyping lymphoma based on the nuclei size and potentially improve reproducibility. Our proposed approach is modular and comprehensible, thus allowing adaptation for specific tasks and increasing the users’ trust in computer-assisted image classification

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Options for sampling and stratification for national forest inventories to implement REDD+ under the UNFCCC

<p>Abstract</p> <p>Background</p> <p>Developing countries that are willing to participate in the recently adopted (16^thSession of the Conference of Parties (COP) in Cancun) mitigation mechanism of Reducing emissions from Deforestation and Forest Degradation - and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks (REDD+) - will have to establish a national forest monitoring system in order to assess anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks. Such a system should support the Measurement, Reporting and Verification (MRV) requirement of the United Nations Framework Convention on Climate Change (UNFCCC) as the REDD+ mechanism is results-based. A national forest inventory (NFI) is one potential key component of such an MRV system. Following the Decision adopted during the 15^thSession of the COP in Copenhagen, the most recent Intergovernmental Panel on Climate Change (IPCC) Guidance and Guidelines should be used as a basis for estimating anthropogenic forest-related greenhouse gas emissions by sources and removals by sinks and changes in forest carbon stocks and area.</p> <p>Results</p> <p>First, we present the key indispensable elements of the IPCC Guidance and Guidelines that have been developed to fulfil the UNFCCC reporting requirements. This is done in order to set the framework to develop the MRV requirement in which a NFI for REDD+ implementation could be developed. Second, within this framework, we develop and propose a novel scheme for the stratification of forest land for REDD+. Finally, we present some non-exhaustive optional elements within this framework that a country could consider to successfully operationalise and implement its REDD+ NFI.</p> <p>Conclusion</p> <p>Evidently, both the methodological guidance and political decisions on REDD+ under the UNFCCC will continue to evolve. Even so, and considering that there exists decades of experience in setting up traditional NFIs, developing a NFI that a country may use to directly support REDD+ activities under the UNFCCC represents the development of a new challenge in this field. It is therefore important that both the scientific community and national implementing agencies acquaint themselves with both the context and content of this challenge so that REDD+ mitigation actions may be implemented successfully and with environmental integrity. This paper provides important contributions to the subject through our proposal of the stratification of forest land for REDD+.</p

International Guidelines for Veterinary Tumor Pathology: A Call to Action

Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as “living documents” on a website (www.vetcancerprotocols.org), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care

Why is the Winner the Best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work

Above-ground carbon stocks, species diversity and fire dynamics in the Bateke Plateau

Savannas are heterogeneous systems characterised by a high spatial and temporal variation in ecosystem structure. Savannas dominate the tropics, with important ecological functions, and play a prominent role in the global carbon cycle, in particular responsible for much of its inter-annual variability. They are shaped by resource availability, soil characteristics and disturbance events, particularly fire. Understanding and predicting the demographic structure and woody cover of savannas remains a challenge, as it is currently poorly understood due to the complex interactions and processes that determine them. A predictive understanding of savanna ecosystems is critical in the context of land use management and global change. Fire is an essential ecological disturbance in savannas, and forest-savanna mosaics are maintained by fire-mediated positive feedbacks. Over half of the world’s savannas are found in Africa, and over a quarter Africa’s surface burns every year, with fires occurring principally in the savanna biome. These have strong environmental and social impacts. Most fires in Africa are anthropogenic and occur during the late dry season, but their dynamics and effects remain understudied. The main objective of this research is to understand the floristic composition, carbon storage, woody cover and fire regime of the mesic savannas of the Bateke Plateau. The Bateke Plateau is savanna-forest mosaic ecosystem, situated mainly in the Republic of Congo, with sandy Kalahari soils and enough precipitation for potential forest establishment (1600 mm/yr). Despite occupying 89,800 km2, its ecology and ecosystem functions are poorly understood. This study combines two approaches: firstly experimental, setting up long term field experiments where the fire regime is manipulated, and then observational, using remote sensing to estimate the carbon storage and study the past history of the fire regime in the region. I established four large (25 ha) plots at two savanna sites, measured their carbon stocks, spatial structure and floristic composition, and applied different annual fire treatments (early and late dry season burns). These treatments were applied annually during 3 years (2015, 2016 and 2017), and the plots were re-measured every year to estimate tree demographic rates and the identification of the key processes that impact them, including fire and competition. Field data were combined with satellite radar data from ALOS PALSAR, and the fire products of the MODIS satellites, to estimate carbon stocks and fire regimes for the entire Bateke Plateau. I also analyse the underlying biophysical and anthropogenic processes that influence the patterns in Above-Ground Woody Biomass (AGWB) and their spatial variability in the Bateke landscape. The total plant carbon stocks (above-ground and below-ground) were low, averaging only 6.5 ± 0.3 MgC/ha, with grass representing over half the biomass. Soil organic matter dominate the ecosystem carbon stocks, with 16.7 ± 0.9 Mg/ha found in the top 20 cm alone. We identified 49 plant species (4 trees, 13 shrubs, 4 sedges, 17 forbs and 11 grass species), with a tree hyperdominance of Hymenocardia acida, and a richer herbaceous species composition. These savannas showed evidence of tree clustering, and also indications of tree-tree competition. Trees had low growth rates (averaging 1.21 mm/yr), and mortality was relatively low (3.24 %/yr) across all plots. The experiment showed that late dry season fires significantly reduced tree growth compared to early dry season fires, but also reduced stem mortality rates. Results show that these mesic savannas had very low tree biomass, with tree cover held far below its climate potential closed-canopy maximum, likely due to nutrient poor sandy soils and frequent fires. Results from the remote sensing analysis indicated that multiple explanatory variables had a significant effect on AGWB in the Bateke Plateau. Overall, the frequency of fire had the largest impact on AGWB (with higher fire frequency resulting in lower AGWB), with sand content the next most important explanatory variable (with more sand reducing AGWB). Fires in the Bateke are very frequent, and show high seasonality. The proportion of fires that occurred in the late dry season, though smaller predictor, was also more important than other factors (including soil carbon proportion, whether or not the savanna area was in a protected area, annual rainfall, or distance to the nearest town, river or road), with a larger proportion of late dry season fires associated with a small increase in AGWB. The results give pointers for management of the savannas of the Bateke Plateau, as well as improving our understanding of vegetation dynamics in this understudied ecosystem and help orient policy and conservation

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The 'typical' lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work