A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas
In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding...
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MDPI AG
2022-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/14/5292 |
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| author | Muhaddisa Barat Ali Xiaohan Bai Irene Yu-Hua Gu Mitchel S. Berger Asgeir Store Jakola |
| author_facet | Muhaddisa Barat Ali Xiaohan Bai Irene Yu-Hua Gu Mitchel S. Berger Asgeir Store Jakola |
| author_sort | Muhaddisa Barat Ali |
| collection | DOAJ |
| description | In most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance. |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:12:54Z |
| publishDate | 2022-07-01 |
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| series | Sensors |
| spelling | doaj.art-d702eb1476124b4a9ec0ac43335eade62023-12-01T22:40:23ZengMDPI AGSensors1424-82202022-07-012214529210.3390/s22145292A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box AreasMuhaddisa Barat Ali0Xiaohan Bai1Irene Yu-Hua Gu2Mitchel S. Berger3Asgeir Store Jakola4Department of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, SwedenDepartment of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, SwedenDepartment of Electrical Engineering, Chalmers University of Technology, 41296 Gothenburg, SwedenDepartment of Neurological Surgery, University of California San Fransisco, San Francisco, CA 94143-0112, USADepartment of Clinical Neuroscience, University of Gothenburg, 40530 Gothenburg, SwedenIn most deep learning-based brain tumor segmentation methods, training the deep network requires annotated tumor areas. However, accurate tumor annotation puts high demands on medical personnel. The aim of this study is to train a deep network for segmentation by using ellipse box areas surrounding the tumors. In the proposed method, the deep network is trained by using a large number of unannotated tumor images with foreground (FG) and background (BG) ellipse box areas surrounding the tumor and background, and a small number of patients (<20) with annotated tumors. The training is conducted by initial training on two ellipse boxes on unannotated MRIs, followed by refined training on a small number of annotated MRIs. We use a multi-stream U-Net for conducting our experiments, which is an extension of the conventional U-Net. This enables the use of complementary information from multi-modality (e.g., T1, T1ce, T2, and FLAIR) MRIs. To test the feasibility of the proposed approach, experiments and evaluation were conducted on two datasets for glioma segmentation. Segmentation performance on the test sets is then compared with those used on the same network but trained entirely by annotated MRIs. Our experiments show that the proposed method has obtained good tumor segmentation results on the test sets, wherein the dice score on tumor areas is (0.8407, 0.9104), and segmentation accuracy on tumor areas is (83.88%, 88.47%) for the MICCAI BraTS’17 and US datasets, respectively. Comparing the segmented results by using the network trained by all annotated tumors, the drop in the segmentation performance from the proposed approach is (0.0594, 0.0159) in the dice score, and (8.78%, 2.61%) in segmented tumor accuracy for MICCAI and US test sets, which is relatively small. Our case studies have demonstrated that training the network for segmentation by using ellipse box areas in place of all annotated tumors is feasible, and can be considered as an alternative, which is a trade-off between saving medical experts’ time annotating tumors and a small drop in segmentation performance.https://www.mdpi.com/1424-8220/22/14/52922D ellipse box areasmulti-stream U-Netbrain tumorsglioma segmentationMR imagesdeep learning |
| spellingShingle | Muhaddisa Barat Ali Xiaohan Bai Irene Yu-Hua Gu Mitchel S. Berger Asgeir Store Jakola A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas Sensors 2D ellipse box areas multi-stream U-Net brain tumors glioma segmentation MR images deep learning |
| title | A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas |
| title_full | A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas |
| title_fullStr | A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas |
| title_full_unstemmed | A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas |
| title_short | A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas |
| title_sort | feasibility study on deep learning based brain tumor segmentation using 2d ellipse box areas |
| topic | 2D ellipse box areas multi-stream U-Net brain tumors glioma segmentation MR images deep learning |
| url | https://www.mdpi.com/1424-8220/22/14/5292 |
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