ArticleInternational journal of computer assisted radiology and surgery2019
Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging.
Article in International journal of computer assisted radiology and surgery, 2019. 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.
- Deep Learning-Based Detection of Malignant and Equivocal Cervical Lymph Nodes on CT Imaging Using a 2D U-Net++ Model.Maedica · 2026Article
- Analysis of deep learning-based segmentation of lymph nodes on full-dose and reduced-dose body CT.Abdominal radiology (New York) · 2026Article
- Article
- Annotation-efficient deep learning detection and measurement of mediastinal lymph nodes in CT.International journal of computer assisted radiology and surgery · 2026Article
- Article
- Deep learning for malignant lymph node segmentation and detection: a review.Frontiers in immunology · 2025Review
- Segmentation of mediastinal lymph nodes in CT with anatomical priors.International journal of computer assisted radiology and surgery · 2024Article
- Segmentation model of soft tissue sarcoma based on self-supervised learning.Frontiers in oncology · 2024Article
- Navigated ultrasound bronchoscopy with integrated positron emission tomography-A human feasibility study.PloS one · 2024Article
- Automatic segmentation of inconstant fractured fragments for tibia/fibula from CT images using deep learning.Scientific reports · 2023Article
- Local Axial Scale-Attention for Universal Lesion Detection.Journal of digital imaging · 2023Article
- Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network.International journal of computer assisted radiology and surgery · 2023Article
- Diagnostic Accuracy of Slow-Capillary Endobronchial Ultrasound Needle Aspiration in Determining PD-L1 Expression in Non-Small Cell Lung Cancer.Advances in respiratory medicine · 2023Article
- Towards automated organs at risk and target volumes contouring: Defining precision radiation therapy in the modern era.Journal of the National Cancer Center · 2022Review
- A bi-directional deep learning architecture for lung nodule semantic segmentation.The Visual computer · 2022Article
- Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images.Medical image analysis · 2022Article
- Dosimetric Study of Deep Learning-Guided ITV Prediction in Cone-beam CT for Lung Stereotactic Body Radiotherapy.Frontiers in public health · 2022Article
- Deep Learning-Assisted Burn Wound Diagnosis: Diagnostic Model Development Study.JMIR medical informatics · 2021Article
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- [Application of semantic segmentation based on convolutional neural network in medical images].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purposeAccurate lung cancer diagnosis is crucial to select the best course of action for treating the patient. From a simple chest CT volume, it is necessary to identify whether the cancer has spread to nearby lymph nodes or not. It is equally important to know precisely where each malignant lymph node is with respect to the surrounding anatomical structures and the airways. In this paper, we introduce a new data-set containing annotations of fifteen different anatomical structures in the mediastinal area, including lymph nodes of varying sizes. We present a 2D pipeline for semantic segmentation and instance detection of anatomical structures and potentially malignant lymph nodes in the mediastinal area.
methodsWe propose a 2D pipeline combining the strengths of U-Net for pixel-wise segmentation using a loss function dealing with data imbalance and Mask R-CNN providing instance detection and improved pixel-wise segmentation within bounding boxes. A final stage performs pixel-wise labels refinement and 3D instance detection using a tracking approach along the slicing dimension. Detected instances are represented by a 3D pixel-wise mask, bounding volume, and centroid position.
resultsWe validated our approach following a fivefold cross-validation over our new data-set of fifteen lung cancer patients. For the semantic segmentation task, we reach an average Dice score of 76% over all fifteen anatomical structures. For the lymph node instance detection task, we reach 75% recall for 9 false positives per patient, with an average centroid position estimation error of 3 mm in each dimension.
conclusionFusing 2D networks' results increases pixel-wise segmentation results while enabling good instance detection. Better leveraging of the 3D information and station mapping for the detected lymph nodes are the next steps.
Indexed as
Identifiers
30891655What 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.