| Autor: |
Michael Gadermayr, Maximilian Tschuchnig, Lea Maria Stangassinger, Christina Kreutzer, Sebastien Couillard-Despres, Gertie Janneke Oostingh, Anton Hittmair |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Computer Methods and Programs in Biomedicine Update, Vol 3, Iss , Pp 100092- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-9900 |
| DOI: |
10.1016/j.cmpbup.2023.100092 |
| Popis: |
Frozen sections are rapidly generated during surgical interventions. This allows surgeons to wait for histological findings during the interventions in order to base intra-surgical decisions on the outcome of the histology. However, compared to paraffin sections the quality of frozen sections is often strongly reduced, leading to a lower diagnostic accuracy. Deep learning-based image translation technology facilitates a virtual conversion between different native imaging technologies with the potential of translating a frozen section into a virtual paraffin section. Stain normalization can be applied to adjust further unequal image characteristics. We investigated the effect of deep learning-based image translation, conventional image normalization and a combination of these techniques on computer aided decision support systems for thyroid cancer diagnostics. For classification, a bag-of-words approach, based on convolutional neural network features, k-means clustering and a support vector machine were employed. While stain normalization led to a decreased overall classification accuracy (0.703 vs 0.727), image translation led to an increased mean score (0.770). A combination of both, image translation and normalization increased the accuracy even further (0.844) and clearly reduced the gap to the post-operative paraffin sections (0.902). Deep learning-based image translation proved to be a powerful tool to enhance accuracy of computer aided diagnosis which clearly outperformed conventional stain translation. This work provides a strong motivation for performing a study with expert pathologists performing the categorization of frozen sections and the corresponding improved sections, to investigate whether a similar effect is achieved in a clinical setting. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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