2026-10-10T23:33:25Z https://www.scidb.cn/oai

10.57760/sciencedb.35531 2026-04-30T14:30:42Z

2026-04-30 Application of 3D nnU-Net Model Based on Dual-Layer Detector CT-Guided Labeling in Non-Contrast CT Esophageal Segmentation: A Comparative Study with the Total Segmentator Model doi:10.57760/sciencedb.35531 en Objective: To explore a 3D nnU Net model based on dual layer CT (DLCT) guided annotation for non contrast CT (NCCT) esophageal tissue with low contrast, difficult segmentation, and limited performance of general segmentation models, in order to improve the accuracy and generalization ability of NCCT esophageal segmentation and compare its performance with the Total Segmentor general model. Method: A retrospective study was conducted on 300 patients who underwent chest DLCT examination at the Fourth Affiliated Hospital of Nanjing Medical University. Virtual non contrast (VNC) and enhanced images were fused to achieve manual segmentation of the esophageal gold standard, and a 3D nnU Net model was trained using VNC. True non contrast (TNC) was used as the internal validation set; Additionally, 100 cases of NCCT from Southeast University Affiliated Zhongda Hospital and 48 cases of NCCT from the National Bioinformatics Center were included as two external test sets. Compare the segmentation performance of the two models using Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), consistency analysis, and Likert 5-point subjective scoring. Results: In the internal validation set and two external test sets, the DSC of our model was 0.756-0.798 and the HD95 was 4.00-4.10mm, both significantly better than the Total Segmentor model (P<0.001); Its consistency with the gold standard (Rspearman=0.821-0.887) and subjective score (5.00-5.00) were also significantly higher than the control model. Conclusion: The 3D nnU Net model based on DLCT guided annotation has better segmentation performance and generalization ability in esophageal segmentation, providing a new technical strategy for NCCT automated segmentation. Model training and source code:Brief code for training a CT-based deep learning segmentation model of thyroid and Papillary thyroid carcinoma using the nnU-Net framework. For more information on running inference with nnU-Net, click here: (https://github.com/MIC-DKFZ/nnUNet)。#0: activate virtual environmentconda create -n nnUNet python=3.10 -yconda activate nnUNetpip install nnunetv2pip install medpy#1: set environment variablesset nnUNet_raw_data_base="path\to\nnUNet_raw_data_base"set nnUNet_preprocessed="path\to\nnUNet_preprocessed"set RESULTS_FOLDER="path\to\nnUNet_trained_models"#2: Task11 (CT), Divide the data set"path\\Scripts\\nnUNetv2_convert_MSD_dataset.exe" -i "D:\nnunet\nnUNet\nnUNet_raw\Task11_example"#3: Preprocessing"path\\Scripts\\nnUNetv2_plan_and_preprocess.exe" -d 11 --verify_dataset_integrity#4: Training 3D fullres models call "path\Scripts\nnUNetv2_train.exe" 11 3d_fullres %%i -tr nnUNetTrainer -device cuda)#5: Test set reasoning 3D fullres"path\\Scripts\\nnUNetv2_predict.exe" -i D:\nnunet\nnUNet\nnUNet_raw\Dataset011_example\imagesTs -o output -d 11 -c 3d_fullres -f all#6: Compute the test set DSC"path\\Scripts\\nnUNetv2_evaluate_folder.exe" -djfile output\dataset.json -pfile output\plans.json D:\nnunet\nnUNet\nnUNet_raw\Task11_example\labelsTs output#7: Computing Test Set HD95"path\\python.exe" calculate_hd95_multilabel.py --pred_folder output --gt_folder D:\nnunet\nnUNet\nnUNet_raw\Task11_example\labelsTs --labels 1,2 --output hd95_multilabel_results.json Esophagus; Segmentation; Non-contrast CT; nnU-Net Hao Wang PUBLIC https://creativecommons.org/licenses/by-nc/4.0/ dataset Science Data Bank