Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network.
Miriam RinneburgerHeike CarolusAndra-Iza IugaMathilda WeisthoffSimon LennartzNils Große HokampLiliana CaldeiraRahil ShahzadDavid MaintzFabian Christopher LaquaBettina BaeßlerTobias KlinderThorsten PersigehlPublished in: European radiology experimental (2023)
• Determination of N status in TNM staging is essential for therapy planning in oncology. • Segmenting cervical lymph nodes manually is highly time-consuming in clinical practice. • Our model provides a robust, automated 3D segmentation of cervical lymph nodes. • It achieves a high accuracy for localization especially of enlarged lymph nodes. • These segmentations should assist clinical care and radiomics research.
Keyphrases
- lymph node
- convolutional neural network
- contrast enhanced
- deep learning
- magnetic resonance imaging
- diffusion weighted
- computed tomography
- magnetic resonance
- neoadjuvant chemotherapy
- sentinel lymph node
- palliative care
- clinical practice
- machine learning
- healthcare
- dual energy
- diffusion weighted imaging
- high throughput
- stem cells
- optical coherence tomography
- image quality
- squamous cell carcinoma
- mass spectrometry
- pain management
- bone marrow
- lymph node metastasis
- liquid chromatography
- smoking cessation