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AUTO.DISTINCT

Financiado

A fully automated deep learning based software for fast robust and accurate det...

A fully automated deep learning based software for fast robust and accurate detection and segmentation of tumours and metastasis 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... ver más

31/03/2022

UNIVERSITEIT MAAST...

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

UNIVERSITEIT MAASTRICHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 2 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-03-31

ERC-2020-POC: Call for proposals for ERC Proof of...

I+D

Cerrada hace 4 años

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2 Participantes

UNIVERSITEIT MAASTRICHT

150.00K€ | Lider

SISTEMAS EXITT SOCIEDAD LIMI...

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Duración del proyecto: 18 meses Fecha Inicio: 2020-09-21
Fecha Fin: 2022-03-31

Líder del proyecto

UNIVERSITEIT MAASTRICHT No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Descripción del proyecto 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.

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