# Mediastinal Lymph Node Detection and Segmentation Using Deep Learning

arXiv: https://arxiv.org/abs/2212.11956 · HTML: https://mlsanity.com/paper/2212.11956/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 24 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [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)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [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)
- [Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding](https://mlsanity.com/paper/2102.06515/) (2021)
- [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)
- [2D View Aggregation for Lymph Node Detection Using a Shallow Hierarchy of Linear Classifiers](https://mlsanity.com/paper/1408.3337/) (2014)

## Built on by (most cited first, 2 of 2)

- [LNQ Challenge 2023: Learning Mediastinal Lymph Node Segmentation with a Probabilistic Lymph Node Atlas](https://mlsanity.com/paper/2406.03984/) (2024)
- [The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review](https://mlsanity.com/paper/2505.06118/) (2025)

## Abstract (arXiv)

Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning technique UNet was modified using bilinear interpolation and total generalized variation (TGV) based upsampling strategy to segment and detect mediastinal lymph nodes. The modified UNet maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution. Collecting CT image data from TCIA, 5-patients, and ELCAP public dataset, a dataset was prepared with the help of experienced medical experts. The UNet was trained using those datasets, and three different data combinations were utilized for testing. Utilizing the proposed approach, the model achieved 94.8% accuracy, 91.9% Jaccard, 94.1% recall, and 93.1% precision on COMBO_3. The performance was measured on different datasets and compared with state-of-the-art approaches. The UNet++ model with hybridized strategy performed better than others.