Description du projet
Améliorer les conséquences du cancer du poumon grâce à une approche automatisée de la caractérisation des tumeurs
Un diagnostic rapide et précis est essentiel pour un traitement efficace de tous les types de cancer. Malheureusement, la détection, la segmentation et la caractérisation avancées des tumeurs dépendent de processus manuels ou semi-manuels laborieux, ce qui limite la précision du traitement et le suivi de la réponse au traitement. Le projet AUTO.DISTINCT financé par l’UE, introduira, démontrera et évaluera un logiciel révolutionnaire, entièrement automatisé, de détection et de segmentation volumétrique rapide, précise, indépendante de l’observateur et reproductible des tumeurs du poumon et des métastases sur les images de tomodensitométrie. Les travaux du projet amélioreront radicalement la caractérisation des tumeurs chez les patients atteints d’un cancer du poumon en affinant la détection des lésions sur les images de tomodensitométrie, ce qui aura des répercussions importantes pour les patients et pour la précision du traitement par radiothérapie.
Objectif
The inaccuracy and inconsistency of state-of-the-art tumour volume detection and segmentation has an adverse influence on patient outcomes. Accurately determining the exact location and volume of tumours is a prerequisite for the detection, segmentation, characterisation and therapy response monitoring for any type of cancer. Today, tumour segmentation is performed manually or semi-automatically in a laborious and time-consuming process that exhibits low accuracy and inconsistency. This compromises quality of care by limiting the certainty of lesion detection on medical images, hindering the effectivity of radiotherapy and restricting the accuracy of treatment response monitoring.
In this ERC PoC project, we introduce fully automated software for fast, accurate, observer independent and reproducible detection and volumetric segmentation of (lung) tumours and metastases on CT images. Through a unique three-step approach, our software demonstrates superior speed, accuracy and robustness of tumour segmentation over both the state-of-the-art as well as published competing solutions for automated tumour segmentation. Hence, our software has the potential to drastically reduce the adverse impact that inaccurate tumour detection and segmentation currently has on (lung) cancer patient outcomes by: improving the detection of lesions on CT images, increasing the accuracy of radiotherapy treatment to reduce the occurrence of geometric misses, and advance the evaluation of tumour response to treatments through volumetric treatment monitoring.
In AUTO.DISTINCT we will provide technical and commercial proof-of-concept for our novel software. We will solve the remaining technical challenges and develop a user-friendly prototype that can be validated with end users. Moreover, we will develop a business strategy that incorporates all technical, commercial, IPR and regulatory aspects of our invention to ensure successful commercialisation.
Champ scientifique
Programme(s)
Régime de financement
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotInstitution d’accueil
6200 MD Maastricht
Pays-Bas