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Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

dc.rights.licenseopenen_US
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
dc.contributor.authorLEFEVRE, Edgar
hal.structure.identifierInstitut de biochimie et génétique cellulaires [IBGC]
dc.contributor.authorBOUILHOL, Emmanuel
hal.structure.identifierCentre de Bioinformatique de Bordeaux [CBIB]
dc.contributor.authorCHAUVIÈRE, Antoine
dc.contributor.authorSOULEYREAU, Wilfried
dc.contributor.authorDERIEPPE, Marie-Alix
hal.structure.identifierCentre de résonance magnétique des systèmes biologiques [CRMSB]
dc.contributor.authorTROTIER, Aurelien
hal.structure.identifierCentre de résonance magnétique des systèmes biologiques [CRMSB]
dc.contributor.authorMIRAUX, Sylvain
IDREF: 150967047
dc.contributor.authorBIKFALVI, Andreas
hal.structure.identifierCentre de résonance magnétique des systèmes biologiques [CRMSB]
dc.contributor.authorRIBOT, Emeline
hal.structure.identifierInstitut de biochimie et génétique cellulaires [IBGC]
dc.contributor.authorNIKOLSKI, Macha
IDREF: 134506049
dc.date.accessioned2022-11-21T11:29:07Z
dc.date.available2022-11-21T11:29:07Z
dc.date.issued2022-10-12
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/170330
dc.description.abstractEnLungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.
dc.language.isoENen_US
dc.title.enDeep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images
dc.typeArticle de revueen_US
dc.identifier.doi10.3389/fbinf.2022.999700en_US
dc.subject.halInformatique [cs]/Bio-informatique [q-bio.QM]en_US
bordeaux.journalFrontiers in Bioinformaticsen_US
bordeaux.volume2en_US
bordeaux.hal.laboratoriesCentre de Résonance Magnétique des Systèmes Biologiques (CRMSB) - UMR 5536en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionCNRSen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcehal
hal.identifierhal-03821458
hal.version1
hal.exportfalse
workflow.import.sourcehal
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Frontiers%20in%20Bioinformatics&rft.date=2022-10-12&rft.volume=2&rft.au=LEFEVRE,%20Edgar&BOUILHOL,%20Emmanuel&CHAUVI%C3%88RE,%20Antoine&SOULEYREAU,%20Wilfried&DERIEPPE,%20Marie-Alix&rft.genre=article


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