Comparison of performances of conventional and deep learning-based methods in segmentation of lung vessels and registration of chest radiographs
| Autor: | Qiming Fang, Qiang Li, Xiaomeng Gu, Wei Guo |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Radiation
medicine.diagnostic_test business.industry Computer science Deep learning Radiography ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image registration Physical Therapy Sports Therapy and Rehabilitation Vessel segmentation Pattern recognition General Medicine Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine 030220 oncology & carcinogenesis medicine Radiology Nuclear Medicine and imaging Segmentation Artificial intelligence business Chest radiograph |
| Zdroj: | Radiological Physics and Technology. 14:6-15 |
| ISSN: | 1865-0341 1865-0333 |
| DOI: | 10.1007/s12194-020-00584-1 |
| Popis: | Conventional machine learning-based methods have been effective in assisting physicians in making accurate decisions and utilized in computer-aided diagnosis for more than 30 years. Recently, deep learning-based methods, and convolutional neural networks in particular, have rapidly become preferred options in medical image analysis because of their state-of-the-art performance. However, the performances of conventional and deep learning-based methods cannot be compared reliably because of their evaluations on different datasets. Hence, we developed both conventional and deep learning-based methods for lung vessel segmentation and chest radiograph registration, and subsequently compared their performances on the same datasets. The results strongly indicated the superiority of deep learning-based methods over their conventional counterparts. |
| Databáze: | OpenAIRE |
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