# Automatic retrieval of corresponding US views in longitudinal examinations

arXiv: https://arxiv.org/abs/2306.04739 · HTML: https://mlsanity.com/paper/2306.04739/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 7 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Big Self-Supervised Models Advance Medical Image Classification](https://mlsanity.com/paper/2101.05224/) (2021)
- [Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik's Cube](https://mlsanity.com/paper/1910.02241/) (2019)
- [Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction](https://mlsanity.com/paper/1907.02757/) (2019)
- [MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models](https://mlsanity.com/paper/2010.05352/) (2020)
- [USCL: Pretraining Deep Ultrasound Image Diagnosis Model through Video Contrastive Representation Learning](https://mlsanity.com/paper/2011.13066/) (2020)
- [A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis](https://mlsanity.com/paper/2108.05930/) (2021)
- [Self-supervised Representation Learning for Ultrasound Video](https://mlsanity.com/paper/2003.00105/) (2020)
- [Contrastive Learning for View Classification of Echocardiograms](https://mlsanity.com/paper/2108.03124/) (2021)
- [Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography](https://mlsanity.com/paper/2102.02287/) (2021)

## Abstract (arXiv)

Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed. Muscle mass must be recovered through physiotherapy before patient discharge and ultrasound imaging is frequently used to assess the recovery process by measuring the muscle size over time. However, these manual measurements are subject to large variability, particularly since the scans are typically acquired on different days and potentially by different operators. In this paper, we propose a self-supervised contrastive learning approach to automatically retrieve similar ultrasound muscle views at different scan times. Three different models were compared using data from 67 patients acquired in the ICU. Results indicate that our contrastive model outperformed a supervised baseline model in the task of view retrieval with an AUC of 73.52% and when combined with an automatic segmentation model achieved 5.7%+/-0.24% error in cross-sectional area. Furthermore, a user study survey confirmed the efficacy of our model for muscle view retrieval.