nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer.
Acad Radiol
; 31(7): 2784-2794, 2024 Jul.
Article
in En
| MEDLINE
| ID: mdl-38350812
ABSTRACT
RATIONALE AND OBJECTIVES:
To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer. MATERIALS ANDMETHODS:
This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists.RESULTS:
The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model 65.5 ± 19.3 cm3 and STAPLE 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88.CONCLUSION:
The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Pancreatic Neoplasms
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Tomography, X-Ray Computed
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Deep Learning
Type of study:
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
Limits:
Adult
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Aged
/
Aged80
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Female
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Humans
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Male
/
Middle aged
Language:
En
Journal:
Acad Radiol
Journal subject:
RADIOLOGIA
Year:
2024
Document type:
Article
Country of publication: