# Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

arXiv: https://arxiv.org/abs/2102.06515 · HTML: https://mlsanity.com/paper/2102.06515/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 11 Feb 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Deeply-Supervised Nets](https://mlsanity.com/paper/1409.5185/) (2014)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Multi-scale self-guided attention for medical image segmentation](https://mlsanity.com/paper/1906.02849/) (2019)
- [Learn To Pay Attention](https://mlsanity.com/paper/1804.02391/) (2018)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network](https://mlsanity.com/paper/1711.08324/) (2017)
- [Abdominal multi-organ segmentation with organ-attention networks and statistical fusion](https://mlsanity.com/paper/1804.08414/) (2018)
- [Brain Tumor Segmentation using an Ensemble of 3D U-Nets and Overall Survival Prediction using Radiomic Features](https://mlsanity.com/paper/1812.01049/) (2018)
- [Lymph Node Gross Tumor Volume Detection and Segmentation via Distance-based Gating using 3D CT/PET Imaging in Radiotherapy](https://mlsanity.com/paper/2008.11870/) (2020)
- [Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem](https://mlsanity.com/paper/2001.11767/) (2020)
- [Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanisms](https://mlsanity.com/paper/2101.07715/) (2021)
- [A 2D dilated residual U-Net for multi-organ segmentation in thoracic CT](https://mlsanity.com/paper/1905.07710/) (2019)
- [Automated cardiovascular magnetic resonance image analysis with fully convolutional networks](https://mlsanity.com/paper/1710.09289/) (2017)

## Built on by (most cited first, 18 of 18)

- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [AeroPath: An airway segmentation benchmark dataset with challenging pathology](https://mlsanity.com/paper/2311.01138/) (2023)
- [LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification](https://mlsanity.com/paper/2408.10069/) (2024)
- [Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors](https://mlsanity.com/paper/2401.06272/) (2024)
- [Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer](https://mlsanity.com/paper/2404.03819/) (2024)
- [Mediastinal Lymph Node Detection and Segmentation Using Deep Learning](https://mlsanity.com/paper/2212.11956/) (2022)
- [LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas](https://mlsanity.com/paper/2406.03984/) (2024)
- [Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation](https://mlsanity.com/paper/2404.08916/) (2024)
- [Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge](https://mlsanity.com/paper/2406.14365/) (2024)
- [Exploiting Scale-Variant Attention for Segmenting Small Medical Objects](https://mlsanity.com/paper/2407.07720/) (2024)
- [Weakly Supervised Lymph Nodes Segmentation Based on Partial Instance Annotations with Pre-trained Dual-branch Network and Pseudo Label Learning](https://mlsanity.com/paper/2408.09411/) (2024)
- [Longitudinal Assessment of Lung Lesion Burden in CT](https://mlsanity.com/paper/2504.06924/) (2025)
- [Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation](https://mlsanity.com/paper/2112.11541/) (2021)
- [A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging](https://mlsanity.com/paper/2301.01448/) (2023)
- [Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation](https://mlsanity.com/paper/2402.06494/) (2024)
- [SDF-Net: A Hybrid Detection Network for Mediastinal Lymph Node Detection on Contrast CT Images](https://mlsanity.com/paper/2409.06324/) (2024)
- [From Slices to Sequences: Autoregressive Tracking Transformer for Cohesive and Consistent 3D Lymph Node Detection in CT Scans](https://mlsanity.com/paper/2503.07933/) (2025)
- [Lesion Detection in CT with Frozen Self-Distilled Features: SALT, a Spatially Adaptive Label-Guided Temperature](https://mlsanity.com/paper/2608.05100/) (2026)

## Abstract (arXiv)

As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. Following the clinical guidelines, estimation of short-axis diameter and mediastinum station are paramount for correct diagnosis. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. Furthermore, the potential impact from simple ensemble strategies is considered. As lymph nodes have similar attenuation values to nearby anatomical structures, we suggest using the knowledge of other organs as prior information to guide the segmentation task. To assess the segmentation and instance detection performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter ≥10 mm, our best performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5, and a segmentation overlap of 80.5%. The method performs similarly well across all stations. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory, given the wide range of expressions a lymph node can exhibit (i.e., shape, location, and attenuation), and contrast uptake variations.