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

Who Is Doing the Job? Unraveling the Role of Ga₂O₃ in Methanol Steam Reforming on Pd₂Ga/Ga₂O₃

A systematic study of the nature, stability, and dynamics of surface species present under methanol steam reforming (MSR) conditions over Pd/Ga₂O₃ and Pd₂Ga/Ga₂O₃ was performed by combining steady state and concentration modulation FTIR spectroscopy. This powerful combination allowed us to obtain novel mechanistic insights into the selective pathway leading to the formation of H₂ and CO₂ and thus to contribute to the understanding of the remarkably different catalytic properties of Pd/Ga₂O₃ and Pd₂Ga/Ga₂O₃. Strongly enhanced formation of adsorbed formates at low temperatures was detected on Pd₂Ga/Ga₂O₃. We ascribe the facilitated formation of these species to the presence of reactive oxygen sites in the Ga₂O₃ surface, which are formed during high-temperature reduction and formation of the intermetallic compound Pd₂Ga. While the stability of involved formates is high under reaction conditions of methanol decomposition (i.e., in the absence of H₂O), the entire adsorption system behaves more dynamically in the presence of water. We propose that the introduction of H₂O into the system converts stable bridging- and bidentate formates into more reactive, monodentate species. These react either with adsorbed methoxy to methyl formate (MFO) in the absence of water or with OH groups supplied by H₂O to CO₂ and H₂. The reaction with OH is faster, leading to a smaller concentration of intermediate monodentate formate under MSR conditions. MFO is easily decomposed into CO and CH₃OH and therefore, it is unlikely to be an intermediate in the selective MSR reaction to CO₂ and H₂. While the formation of intermetallic particles by high-temperature reduction is a prerequisite to achieving high MSR selectivity, our results suggest that the reaction sequence predominantly proceeds on the Ga₂O₃ surface, that is modified by the high temperature reduction and the formation of Pd₂Ga, and is only promoted by the presence of the intermetallic particles

Microstructural Changes of Supported Intermetallic Nanoparticles under Reductive and Oxidative Conditions: An in Situ X‑ray Absorption Study of Pd/Ga₂O₃

In this work, the structure and stability of Pd–Ga intermetallic nanoparticles under various reactive conditions was investigated by combining in situ X-ray absorption spectroscopy (XAS), FTIR of CO adsorption, and XRD. By in situ XAS we followed in detail the formation of Pd–Ga intermetallic compounds (IMC) upon reduction of Pd/Ga₂O₃, which was observed to be a rather slow process that depends on the availability of reduced Ga formed by the atomic H provided by Pd. Using crystal structures of a variety of Pd–Ga IMCs, we have identified Pd₂Ga as the compound that is formed during reduction at 623 K. In contrast to Pd/Ga₂O₃, β-hydride formation did not occur once Pd₂Ga particles are formed, as evidenced by the absence of lattice expansion in hydrogen atmosphere. However, XAS revealed that Pd₂Ga is not stable in oxygen already at room temperature. Although XRD showed no bulk structural modification, CO adsorption on an oxygen exposed catalyst detected a metallic Pd surface, partly decorated with oxidic Ga. Only in situ XAS provided clear indications on the structural modification occurring upon oxygen exposure, showing that the overall state of the sample is a mixture of Pd or a Ga-depleted IMC and Pd₂Ga. Based on these observations, Ga segregation from the surface-near region to the surface, followed by oxidation, was concluded. The intermetallic surface is easily reformed by reduction, due to remaining Pd at the surface activating H₂

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\u27 trust in computer-assisted image classification