Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation
With the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-10-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2020.1787678 |
| _version_ | 1797684878409465856 |
|---|---|
| author | Brad Niepceron Ahmed Nait-Sidi-Moh Filippo Grassia |
| author_facet | Brad Niepceron Ahmed Nait-Sidi-Moh Filippo Grassia |
| author_sort | Brad Niepceron |
| collection | DOAJ |
| description | With the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep Learning methods that require the use of powerful dedicated hardware to be executed and address a power consumption problem that is not compatible with the aforementioned requests. There is thus a demand in the development of low-cost image analysis systems with increased performances. In this work, we address this problem by proposing a fully-automatic brain tumor segmentation method based on a Convolutional Neural Network, executed by a low-cost, Deep Learning ready GPU embedded platform. We validated our approach using the BRaTS 2015 dataset to segment brain tumors and proved that an artificial neural network can be trained and used in the medical field with limited resources by redefining some of its inner operations. |
| first_indexed | 2024-03-12T00:36:07Z |
| format | Article |
| id | doaj.art-61094a20f6064d82888a4a3d25a3d9a8 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:07Z |
| publishDate | 2020-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-61094a20f6064d82888a4a3d25a3d9a82023-09-15T09:33:58ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452020-10-01341286687910.1080/08839514.2020.17876781787678Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor SegmentationBrad Niepceron0Ahmed Nait-Sidi-Moh1Filippo Grassia2University of Picardie Jules VerneUniversity of Picardie Jules VerneUniversity of Picardie Jules VerneWith the growth of medical data stored as bases for researches and diagnosis tasks, healthcare providers are in need of automatic processing methods to make accurate and fast image analysis such as segmentation or restoration. Most of the existing solutions to deal with these tasks are based on Deep Learning methods that require the use of powerful dedicated hardware to be executed and address a power consumption problem that is not compatible with the aforementioned requests. There is thus a demand in the development of low-cost image analysis systems with increased performances. In this work, we address this problem by proposing a fully-automatic brain tumor segmentation method based on a Convolutional Neural Network, executed by a low-cost, Deep Learning ready GPU embedded platform. We validated our approach using the BRaTS 2015 dataset to segment brain tumors and proved that an artificial neural network can be trained and used in the medical field with limited resources by redefining some of its inner operations.http://dx.doi.org/10.1080/08839514.2020.1787678 |
| spellingShingle | Brad Niepceron Ahmed Nait-Sidi-Moh Filippo Grassia Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation Applied Artificial Intelligence |
| title | Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation |
| title_full | Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation |
| title_fullStr | Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation |
| title_full_unstemmed | Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation |
| title_short | Moving Medical Image Analysis to GPU Embedded Systems: Application to Brain Tumor Segmentation |
| title_sort | moving medical image analysis to gpu embedded systems application to brain tumor segmentation |
| url | http://dx.doi.org/10.1080/08839514.2020.1787678 |
| work_keys_str_mv | AT bradniepceron movingmedicalimageanalysistogpuembeddedsystemsapplicationtobraintumorsegmentation AT ahmednaitsidimoh movingmedicalimageanalysistogpuembeddedsystemsapplicationtobraintumorsegmentation AT filippograssia movingmedicalimageanalysistogpuembeddedsystemsapplicationtobraintumorsegmentation |