Abstract PR-06: Utilizing biological domain knowledge and machine learning methods to improve cellular segmentation on multiplex fluorescence and imaging mass cytometry datasets improves the quality of single-cell data obtained
Notice bibliographique
Résumé
Abstract Currently, many existing multi-modality image analysis methods rely heavily on manual annotations for the extraction of quantitative readouts. This limits both the analytical throughput and the complexity to which one can analyze the data – for every hour of imaging time, several additional hours of tedious manual labor are require to extract even rudimentary metrics. Recent advances in computer vision-based techniques, including the use of machine and deep learning methods, have shown tremendous promise for solving previously intractable image segmentation and classification problems. This presents an opportunity for the development of image analysis pipelines that utilize these technical advances to solve known biomedical imaging challenges. We have developed a series of image analysis pipelines for multiplex image segmentation that draws from several interdisciplinary collaborations between biological scientists, pathologists, and biomedical image analysis specialists. For example, we have recently utilized biomedical domain knowledge to develop a customized cellular segmentation methodology for Imaging Mass Cytometry, to identify invading immune cells of various biomarker-identified types, and measure their density and proximity to blood vessels (a marker of active invasion) in multiple sclerosis lesions in the brain. A robust image segmentation and classification pipeline permits us to move these complex datasets from the format of “pixels embedded in spatial coordinates” towards the format of a “single-cell proteomic” dataset, that also permits spatial relationships between markers or tissue regions to be queried. Likewise, we have illustrated a number of methods for single-cell and per-vessel analysis of hypoxia and proliferation gradients within solid tumor tissue sections. Development of single-cell “tissue cytometric” methods permits the in-depth study of spatial relationships that would be difficult or impossible to quantify with more rudimentary whole-tissue analysis approaches. The spatial relationships between tissue components are tightly integrated with tissue metabolism and biomedical transport phenomena, so the study of these relationships permits us to better model tissue physiology in silico, through more accurate measurement of relevant physiological parameters. Clinical validation and deployment of analytical methodologies developed in the laboratory, along with robust methods for quality control and validation of the analytical outputs, allows us to move these promising research tools towards the ultimate goal of clinically approved diagnostic algorithms and medical devices. Such efforts hold the promise to deliver a profound positive impact on the healthcare system, reducing tedious manual steps like counting cells or reviewing scans, through an optimal combination of the advantages of validated automated methods with clinical wisdom and experience. Citation Format: Trevor D. McKee, Mark Zaidi, Veronica Cojocari. Utilizing biological domain knowledge and machine learning methods to improve cellular segmentation on multiplex fluorescence and imaging mass cytometry datasets improves the quality of single-cell data obtained [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-06.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».