ArticleBMC bioinformatics2021
Design of lung nodules segmentation and recognition algorithm based on deep learning.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 65 citations in OpenAlex.
- Review
- Federated lung nodule segmentation using a hybrid transformer-U-Net architecture.Scientific reports · 2026Article
- Deep learning-based assessment of periapical radiographic image quality.Scientific reports · 2026Article
- Artificial Intelligence and Machine Learning in Lung Cancer: Advances in Imaging, Detection, and Prognosis.Cancers · 2025Review
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.Scientific reports · 2024Article
- Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.International journal of computer assisted radiology and surgery · 2024Article
- A Multi-Task Model for Pulmonary Nodule Segmentation and Classification.Journal of imaging · 2024Article
- Study on lung CT image segmentation algorithm based on threshold-gradient combination and improved convex hull method.Scientific reports · 2024Article
- Lung Cancer Surgery in Octogenarians: Implications and Advantages of Artificial Intelligence in the Preoperative Assessment.Healthcare (Basel, Switzerland) · 2024Review
- Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis.Cancer medicine · 2024Review
- Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- An improved V-Net lung nodule segmentation model based on pixel threshold separation and attention mechanism.Scientific reports · 2024Article
- A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment.Frontiers in plant science · 2024Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- An Optimization Method of Deep Transfer Learning for Vegetation Segmentation under Rainy and Dry Season Differences in a Dry Thermal Valley.Plants (Basel, Switzerland) · 2023Article
- Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis.Insights into imaging · 2023Review
- Deep learning-based prediction of mandibular growth trend in children with anterior crossbite using cephalometric radiographs.BMC oral health · 2023Article
- Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.BMC medical imaging · 2022Article
- SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition.Frontiers in genetics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundAccurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer. An algorithm is proposed for 3D CT sequence images in this paper based on 3D Res U-Net segmentation network and 3D ResNet50 classification network. The common convolutional layers in encoding and decoding paths of U-Net are replaced by residual units while the loss function is changed to Dice loss after using cross entropy loss to accelerate network convergence. Since the lung nodules are small and rich in 3D information, the ResNet50 is improved by replacing the 2D convolutional layers with 3D convolutional layers and reducing the sizes of some convolution kernels, 3D ResNet50 network is obtained for the diagnosis of benign and malignant lung nodules.
results3D Res U-Net was trained and tested on 1044 CT subcases in the LIDC-IDRI database. The segmentation result shows that the Dice coefficient of 3D Res U-Net is above 0.8 for the segmentation of lung nodules larger than 10 mm in diameter. 3D ResNet50 was trained and tested on 2960 lung nodules in the LIDC-IDRI database. The classification result shows that the diagnostic accuracy of 3D ResNet50 is 87.3% and AUC is 0.907.
conclusionThe 3D Res U-Net module improves segmentation performance significantly with the comparison of 3D U-Net model based on residual learning mechanism. 3D Res U-Net can identify small nodules more effectively and improve its segmentation accuracy for large nodules. Compared with the original network, the classification performance of 3D ResNet50 is significantly improved, especially for small benign nodules.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.