Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hinterwimmer:2023:10.1007/s00330-022-09184-6,
author = {Hinterwimmer, F and Consalvo, S and Wilhelm, N and Seidl, F and Burgkart, RHH and von, Eisenhart-Rothe R and Rueckert, D and Neumann, J},
doi = {10.1007/s00330-022-09184-6},
journal = {Eur Radiol},
pages = {1537--1544},
title = {SAM-X: sorting algorithm for musculoskeletal x-ray radiography.},
url = {http://dx.doi.org/10.1007/s00330-022-09184-6},
volume = {33},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity. METHODS: In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model. A human-in-the-loop radiologist assigned weak, semantic labels to all clusters and clusters with the same label were merged. Three hundred thirty-two non-musculoskeletal clusters were discarded. In phase 2, the initial model was modified by "injecting" the identified labels into the self-supervised model to train a classifier. To provide statistical significance, data split and cross-validation were applied. The hold-out test set consisted of 50% external data. To gain insight into the model's predictions, Grad-CAMs were calculated. RESULTS: The self-supervised clustering resulted in a high normalized mutual information of 0.930. The expert radiologist identified 28 musculoskeletal clusters. The modified model achieved a classification accuracy of 96.2% and 96.6% for validation and hold-out test data for predicting the top class, respectively. When considering the top two predicted class labels, an accuracy of 99.7% and 99.6% was accomplished. Grad-CAMs as well as final cluster results underlined the robustness of the proposed method by showing that it focused on similar image regions a human would have considered for categorizing images. CONCLUSION: For efficient dataset building, we propose an accurate deep learning sorting algorithm for classifying radiographs according to their anatomical entity in the assessment of musculoskeletal diseases. KEY POINTS: • Classification of large radiograph datasets according to their anatomical entity. • Paramount importance of structuring vast amounts of retrospective data for modern deep learning app
AU  - Hinterwimmer,F
AU  - Consalvo,S
AU  - Wilhelm,N
AU  - Seidl,F
AU  - Burgkart,RHH
AU  - von,Eisenhart-Rothe R
AU  - Rueckert,D
AU  - Neumann,J
DO  - 10.1007/s00330-022-09184-6
EP  - 1544
PY  - 2023///
SP  - 1537
TI  - SAM-X: sorting algorithm for musculoskeletal x-ray radiography.
T2  - Eur Radiol
UR  - http://dx.doi.org/10.1007/s00330-022-09184-6
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36307553
VL  - 33
ER  -
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