MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.
Comput Med Imaging Graph
; 92: 101957, 2021 09.
Article
in English
| MEDLINE | ID: covidwho-1330724
ABSTRACT
Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation from CT is therefore very important for correct diagnosis and treatment planning. The automated lung tumor segmentation is challenging due to the high variance in appearance and shape of the targeting tumors. To overcome the challenge, we present an effective 3D U-Net equipped with ResNet architecture and a two-pathway deep supervision mechanism to increase the network's capacity for learning richer representations of lung tumors from global and local perspectives. Extensive experiments on two real medical datasets the lung CT dataset from Liaoning Cancer Hospital in China with 220 cases and the public dataset of TCIA with 422 cases. Our experiments demonstrate that our model achieves an average dice score (0.675), sensitivity (0.731) and F1-score (0.682) on the dataset from Liaoning Cancer Hospital, and an average dice score (0.691), sensitivity (0.746) and F1-score (0.724) on the TCIA dataset, respectively. The results demonstrate that the proposed 3D MSDS-UNet outperforms the state-of-the-art segmentation models for segmenting all scales of tumors, especially for small tumors. Moreover, we evaluated our proposed MSDS-UNet on another challenging volumetric medical image segmentation task COVID-19 lung infection segmentation, which shows consistent improvement in the segmentation performance.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Radiographic Image Interpretation, Computer-Assisted
/
Tomography, X-Ray Computed
/
Imaging, Three-Dimensional
/
Supervised Machine Learning
/
COVID-19
/
Lung Neoplasms
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Comput Med Imaging Graph
Journal subject:
Diagnostic Imaging
Year:
2021
Document Type:
Article
Affiliation country:
J.compmedimag.2021.101957
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