# Re-Identification and Growth Detection of Pulmonary Nodules without Image Registration Using 3D Siamese Neural Networks

arXiv: https://arxiv.org/abs/1912.10525 · HTML: https://mlsanity.com/paper/1912.10525/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Transformer Lesion Tracker](https://mlsanity.com/paper/2206.06252/) (2022)
- [An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth](https://mlsanity.com/paper/2104.08789/) (2021)
- [Siamese Encoder-based Spatial-Temporal Mixer for Growth Trend Prediction of Lung Nodules on CT Scans](https://mlsanity.com/paper/2206.03049/) (2022)
- [Detection, growth quantification and malignancy prediction of pulmonary nodules using deep convolutional networks in follow-up CT scans](https://mlsanity.com/paper/2103.14537/) (2021)
- [Automatic nodule identification and differentiation in ultrasound videos to facilitate per-nodule examination](https://mlsanity.com/paper/2310.06339/) (2023)
- [Multi-view user representation learning for user matching without personal information](https://mlsanity.com/paper/2312.14533/) (2023)
- [Towards AI Lesion Tracking in PET/CT Imaging: A Siamese-based CNN Pipeline applied on PSMA PET/CT Scans](https://mlsanity.com/paper/2406.09327/) (2024)

## Abstract (arXiv)

Lung cancer follow-up is a complex, error prone, and time consuming task for clinical radiologists. Several lung CT scan images taken at different time points of a given patient need to be individually inspected, looking for possible cancerogenous nodules. Radiologists mainly focus their attention in nodule size, density, and growth to assess the existence of malignancy. In this study, we present a novel method based on a 3D siamese neural network, for the re-identification of nodules in a pair of CT scans of the same patient without the need for image registration. The network was integrated into a two-stage automatic pipeline to detect, match, and predict nodule growth given pairs of CT scans. Results on an independent test set reported a nodule detection sensitivity of 94.7%, an accuracy for temporal nodule matching of 88.8%, and a sensitivity of 92.0% with a precision of 88.4% for nodule growth detection.