Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function

Bianca Lassen-Schmidt; Alessa Denise Hering; Stefan Krass; Hans  Meine

doi:https://doi.org/10.48550/arXiv.2006.00083

Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function

Bianca Lassen-Schmidt, Alessa Denise Hering, Stefan Krass, Hans Meine

Institut für Mathematische Methoden der Bildverarbeitung

Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359 Bremen

Abstract

Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting).

Originalsprache	Englisch
DOIs	https://doi.org/10.48550/arXiv.2006.00083
Publikationsstatus	Veröffentlicht - 29.05.2020
Veranstaltung	Medical Imaging with Deep Learning 2020 - Montréal, Montréal, Kanada Dauer: 06.07.2020 → 09.07.2020 https://2020.midl.io/

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress	Medical Imaging with Deep Learning 2020
Kurztitel	MIDL 2020
Land/Gebiet	Kanada
Ort	Montréal
Zeitraum	06.07.20 → 09.07.20
Internetadresse	https://2020.midl.io/

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https://doi.org/10.48550/arXiv.2006.00083

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title = "Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function",

abstract = "Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting). ",

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AU - Lassen-Schmidt, Bianca

AU - Hering, Alessa Denise

AU - Krass, Stefan

AU - Meine, Hans

PY - 2020/5/29

Y1 - 2020/5/29

N2 - Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting).

AB - Fully-automatic lung lobe segmentation is challenging due to anatomical variations, pathologies, and incomplete fissures. We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries. To validate the performance of the proposed method we compared the results to two other methods. The new loss function improved the mean distance to 1.46 mm (compared to 2.08 mm for simple loss function without weighting).

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DO - https://doi.org/10.48550/arXiv.2006.00083

M3 - Conference Papers

T2 - Medical Imaging with Deep Learning 2020

Y2 - 6 July 2020 through 9 July 2020

ER -