Automated segmentation of endometrial cancer on MR images using deep learning
| Autor: | Øyvind Salvesen, Kari Strøno Wagner-Larsen, Camilla Krakstad, Julie Andrea Dybvik, Antonella Zanna Munthe-Kaas, Alexander Lundervold, Arvid Lundervold, Erlend Hodneland, Bradley J. Erickson, Kristine Eldevik Fasmer, Veronika Solteszova, Ingfrid S. Haldorsen |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Computer science Science Convolutional neural network Article 030218 nuclear medicine & medical imaging 03 medical and health sciences Automation 0302 clinical medicine Deep Learning medicine Adjuvant therapy Image Processing Computer-Assisted Humans Segmentation Multidisciplinary business.industry Deep learning Endometrial cancer Computational science medicine.disease Mr imaging Magnetic Resonance Imaging Endometrial Neoplasms Tumor Burden 030220 oncology & carcinogenesis Medicine Cancer imaging Female Artificial intelligence Radiology Mr images business |
| Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
| Popis: | Preoperative MR imaging in endometrial cancer patients provides valuable information on local tumor extent, which routinely guides choice of surgical procedure and adjuvant therapy. Furthermore, whole-volume tumor analyses of MR images may provide radiomic tumor signatures potentially relevant for better individualization and optimization of treatment. We apply a convolutional neural network for automatic tumor segmentation in endometrial cancer patients, enabling automated extraction of tumor texture parameters and tumor volume. The network was trained, validated and tested on a cohort of 139 endometrial cancer patients based on preoperative pelvic imaging. The algorithm was able to retrieve tumor volumes comparable to human expert level (likelihood-ratio test, $$p = 0.06$$ p = 0.06 ). The network was also able to provide a set of segmentation masks with human agreement not different from inter-rater agreement of human experts (Wilcoxon signed rank test, $$p=0.08$$ p = 0.08 , $$p=0.60$$ p = 0.60 , and $$p=0.05$$ p = 0.05 ). An automatic tool for tumor segmentation in endometrial cancer patients enables automated extraction of tumor volume and whole-volume tumor texture features. This approach represents a promising method for automatic radiomic tumor profiling with potential relevance for better prognostication and individualization of therapeutic strategy in endometrial cancer. |
| Databáze: | OpenAIRE |
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