Object orientated automated image analysis: quantitative and qualitative estimation of inflammation in mouse lung

Historically, histopathology evaluation is performed by a pathologist generating a qualitative assessment on thin tissue sections on glass slides. In the past decade, there has been a growing interest for tools able to reduce human subjectivity and improve workload. Whole slide scanning technology combined with object orientated image analysis can offer the capacity of generating fast and reliable results. In the present study, we combined the use of these emerging technologies to characterise a mouse model for chronic asthma. We monitored the inflammatory changes over five weeks by measuring the number of neutrophils and eosinophils present in the tissue, as well as, the bronchiolar associated lymphoid tissue (BALT) area on whole lungs sections. We showed that inflammation assessment could be automated efficiently and reliably. In comparison to human evaluation performed on the same set of sections, computer generated data was more descriptive and fully quantitative. Moreover optimisation of our detection parameters allowed us to be to more sensitive and to generate data in a larger dynamic range to traditional experimental evaluation, such as bronchiolar lavage (BAL) inflammatory cell counts obtained by flow cytometry. We also took advantage of the fact that we could increase the number of samples to be analysed within a day. Such optimisation allowed us to determine the best study design and experimental conditions in order to increase statistical significance between groups. In conclusion, we showed that combination of whole slide digital scanning and image analysis could be fully automated and deliver more descriptive and biologically relevant data over traditional methods evaluating histopathological pulmonary changes observed in this mouse model of chronic asthma

The Process of Digital Pathology and its Application in a Study

Digital pathology saw its advent in the 60’s with the introduction of telepathology and was brought into a brighter spotlight in the late 90’s through the technological breakthrough in histopathological imaging, called whole slide imaging (WSI). With steady growth in interest among experts, the latest breakthrough in WSI happened in 2017, when both the US Food and Drug Administration and the European Union approved the use of WSI systems in primary diagnostics. So far, the adoption of digital pathology has been slower than many expected, but many laboratories around the world are looking to switch into a digital workflow. In this text, I aim to describe the history and the technical basics of digital pathology and WSI, as well as discuss some of its most widely used and promising applications in education, research, telepathology, clinical work, and image analysis. To better illuminate the digital workflow, I describe the use of digital pathology in a study by Anttinen M et al., in which the author of this text participated in the form of digitizing the whole slide images used in the study. With the advancements in digital pathology in the past two decades and with the regulation catching up, wider adoption WSI systems is to be expected. Many advantages can be associated with digital pathology e.g., better results in learning for students, cost reductions in clinical work, and the reduction in pathologists’ workload due to automated image analysis methods

Computational Analysis of Tumour Microenvironment in mIHC Stained Diffuse Glioma Samples

Healthcare is a sector that has been notoriously stagnant in digital innovation, nevertheless its transformation is imminent. Digital pathology is a field that is being accentuated in light of recent technological development. With capacity to conduct high-resolution tissue imaging and managing output digitally, advanced image analysis and Machine Learning can be subsequently applied. These methods provide means to for instance automating segmentation of region-of-interests, diagnosis and knowledge discovery. Brain malignancies are particularly dire with a high fatality rate and relatively high occurrence in children. Diffuse gliomas are a subtype of brain tumours whose biological behavior range from very indolent to extremely aggressive, which is reflected in grading I - IV. The brain tumour micro-environment (TME) --- local area surrounding cancerous cells with a plethora of immune cells and other structures in interaction --- has emerged as a critical regulator of brain tumour progression. Researchers are interested in immunotherapeutic treatment of brain cancer, since modern approaches are insufficient in treatment of especially the most aggressive tumours. Additionally, the TME is rendered difficult to understand. Multiplex Immunohistochemistry (mIHC) is a novel approach in effectively mapping spatial distribution of cell types in tissue samples using multiple antibodies. In this thesis, we investigate the TME in diffuse glioma mIHC samples for three patient cases with 2-3 differing tumour grades per patient. From the 18 possibilities we selected 6 antigens (markers) of interest for further analysis. In particular, we are interested in how relative proportion of positive antigens and mean distance to nearest blood vessel vary for our selected markers in tumour progression. In order to acquire desired properties, we register each corresponding image, detect nuclei, segment cells and extract structured data from region channel intensities along with their location and distance to nearest blood vessel. Our primary finding is that M2-macrophage and T cell occurrence proportions as well as their mean distance to blood vessel grow with increasing tumour grade. The results could suggest that aforementioned cell types are of low quantity in near vicinity of blood vessels in low tumour grades, and conversely with higher quantities and more homogeneous distribution in aggressive tumours. Despite the several potential error sources and non-standardized processes in the pipeline between tissue extraction and image analysis, our results support pre-existing knowledge in that M2-macrophage proportion has a positive correlation with tumour grade.Terveydenhuollon digitaalinen kehitys on ollut hidasliikkeistä muihin sektoreihin verrattuna. Tästä huolimatta, terveydenhuollon digitaalinen muunnos on välitön ja asiaan liittyvä tutkimus jatkuvaa. Digitaalinen patologia on ala, joka viime aikaisen teknologisen kehityksen myötä on korostunut. Kudoskuvantaminen korkealla resoluutiolla ja näytteiden digitaalinen hallinta on mahdollistanut kehittyneen kuvanalysiin sekä koneoppimisen soveltamisen. Nämä metodit luovat keinot esimerkiksi biologisesti merkittävien alueiden segmentointiin, diagnoosiin ja uuden tieteellisen tiedon tuottamiseen. Aivokasvaimet ovat järkyttäviä, sillä tapauskuolleisuus ja esiintymä nuorissa ovat suhteellisen korkealla. Diffuusigliomat ovat aivokasvainten alatyyppi, jonka sisältämät kasvaimet luokitellaan niiden aggressiivisuuden perusteella eri graduksiin väliltä I - IV. Kasvaimen mikroympäristö (TME), eli syöpäsolujen paikallinen ympäristö sisältäen mm. runsaasti immuunipuolustuksen soluja vuorovaikutuksessa, on osoittautunut merkittäväksi tekijäksi kasvaimen kehityksen suhteen. Aivosyövän tutkimus painottuu immunoterapeuttisiin ratkaisuihin, sillä nykyiset hoitomuodot eivät ole tarpeeksi tehokkaita etenkään kaikista aggressiivisimpien kasvainten hoidossa. Lisäksi mikroympäristö voi olla vaikea ymmärtää. Monikanavainen immunohistokemiallinen värjäys (mIHC) on uudenlainen lähestymistapa solutyyppien spatiaalijakauman kartoittamiseen kudosnäytteissä tehokkaasti hyödyntäen useita vasta-aineita. Tässä opinnäytetyössä tutkitaan diffuusigliooma mIHC-näytteitä kolmelle potilastapaukselle. Jokaista potilasta kohti on 2-3 näytettä eri kasvainlaaduista ja yhteensä 18 mIHC-kanavaa per näyte, joista 6 otettiin tarkasteluun. Tarkalleen ottaen, solutyyppien aktivaatioiden osuudet positiivisten antigeenien perusteella ja keskimääräinen etäisyys lähimpään verisuoneen jokaista ryhmää kohti lasketaan eri kasvaimen laaduissa. Tavoitteen saavuttamiseksi näytteitä vastaavat kuvat rekisteröidään, tumat tunnistetaan, solualueet segmentoidaan ja kerätään jäsenneltyä tietoa alueiden intensiteettikanavista mukaan lukien sijainti ja sijaintia vastaava etäisyys lähimpään verisuoneen. Pääasiallinen löytö on, että M2-makrofagien ja T-solujen suhteelliset osuudet sekä keskimääräinen etäisyys lähimpään verisuoneen nousevat kasvaimen ollessa aggressiivisempi. Tulokset saattavat ehdottaa, että edellämainitut solutyypit ovat vähäisiä ja verisuonten lähellä kun kasvain on hyvänlaatuinen ja vastaavasti suurimilla osuuksilla ja enemmän homogeenisesti jakautunut kun kasvain on aggressiivisempi. Useista virhelähteistä ja kudosanalyysin liittyvistä ei-standardisoiduista prosesseista huolimatta, tuloksemme tukevat ennaltatiedettyä tietoa siitä, että M2-makrofagien osuudella on positiivinen korrelaatio kasvaimen laatuun

Unitopatho, A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading

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Vessels Classification in Retinal Images by Graph-Based Approach

The classification of retinal vessels into artery/vein (A/V) is an important phase for automating the detection of vascular changes. This paper presents an automatic approach for A/V classification based on the analysis of a graph extracted from the retinal vasculature. Classifier classifies the entire vascular tree deciding on the type of each intersection point (graph nodes) and assigning one of two labels to each vessel segment (graph links). Final classification of a vessel segment as A/V is performed through the combination of the graph-based labeling results with a set of intensity features. Our method out performs recent approaches for A/V classification. Normal retinal images vessels are segmented using the morphological operations and then using graph trace algorithm for identification the center line of the vessels and trace the pixel values as a feature and use the KNN classifier to classify the feature and assign which is the artery and which is the vein in retinal image. From features we extract the thickness of the vessels to identify the disease details. DOI: 10.17762/ijritcc2321-8169.150316

Automation in the clinical microbiology laboratory.

Automation in the clinical microbiology laborator

xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure