# Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks

arXiv: https://arxiv.org/abs/2206.06730 · HTML: https://mlsanity.com/paper/2206.06730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 15 of 15)

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- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://mlsanity.com/paper/1611.09326/) (2016)
- [3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes](https://mlsanity.com/paper/1809.00076/) (2018)
- [A deep learning model integrating FCNNs and CRFs for brain tumor segmentation](https://mlsanity.com/paper/1702.04528/) (2017)
- [Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy](https://mlsanity.com/paper/1707.05137/) (2017)
- [XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets](https://mlsanity.com/paper/1812.00548/) (2018)
- [Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data](https://mlsanity.com/paper/1806.00921/) (2018)
- [Computer-Aided Assessment of Catheters and Tubes on Radiographs: How Good is Artificial Intelligence for Assessment?](https://mlsanity.com/paper/2002.03413/) (2020)
- [Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays](https://mlsanity.com/paper/1907.01656/) (2019)
- [Robust Brain Magnetic Resonance Image Segmentation for Hydrocephalus Patients: Hard and Soft Attention](https://mlsanity.com/paper/2001.03857/) (2020)

## Abstract (arXiv)

Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip. This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its disposition. To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. The performance was compared by each root mean squared error (RMSE) and MFP incidence rate according to whether or not MFCN is applied to five conventional models. In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45%. The RMSE was improved by over 63% from an average of 26.85mm (17.16 to 35.80mm) to 9.72mm (9.37 to 10.98mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65%. Therefore, by applying the proposed MFCN, we observed the significant/consistent detection performance improvement of PICC tip location compared to the existing model.