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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2408.14847 |
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| _version_ | 1866929475719004160 |
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| author | Kassam, Samir Markham, Angelo Vo, Katie Revanakara, Yashas Lam, Michael Zhu, Kevin |
| author_facet | Kassam, Samir Markham, Angelo Vo, Katie Revanakara, Yashas Lam, Michael Zhu, Kevin |
| contents | Gliomas, a common type of malignant brain tumor, present significant surgical challenges due to their similarity to healthy tissue. Preoperative Magnetic Resonance Imaging (MRI) images are often ineffective during surgery due to factors such as brain shift, which alters the position of brain structures and tumors. This makes real-time intraoperative MRI (ioMRI) crucial, as it provides updated imaging that accounts for these shifts, ensuring more accurate tumor localization and safer resections. This paper presents a deep learning pipeline combining You Only Look Once Version 8 (YOLOv8) and Segment Anything Model Vision Transformer-base (SAM ViT-b) to enhance glioma detection and segmentation during ioMRI. Our model was trained using the Brain Tumor Segmentation 2021 (BraTS 2021) dataset, which includes standard magnetic resonance imaging (MRI) images, and noise-augmented MRI images that simulate ioMRI images. Noised MRI images are harder for a deep learning pipeline to segment, but they are more representative of surgical conditions. Achieving a Dice Similarity Coefficient (DICE) score of 0.79, our model performs comparably to state-of-the-art segmentation models tested on noiseless data. This performance demonstrates the model's potential to assist surgeons in maximizing tumor resection and improving surgical outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_14847 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection Kassam, Samir Markham, Angelo Vo, Katie Revanakara, Yashas Lam, Michael Zhu, Kevin Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Gliomas, a common type of malignant brain tumor, present significant surgical challenges due to their similarity to healthy tissue. Preoperative Magnetic Resonance Imaging (MRI) images are often ineffective during surgery due to factors such as brain shift, which alters the position of brain structures and tumors. This makes real-time intraoperative MRI (ioMRI) crucial, as it provides updated imaging that accounts for these shifts, ensuring more accurate tumor localization and safer resections. This paper presents a deep learning pipeline combining You Only Look Once Version 8 (YOLOv8) and Segment Anything Model Vision Transformer-base (SAM ViT-b) to enhance glioma detection and segmentation during ioMRI. Our model was trained using the Brain Tumor Segmentation 2021 (BraTS 2021) dataset, which includes standard magnetic resonance imaging (MRI) images, and noise-augmented MRI images that simulate ioMRI images. Noised MRI images are harder for a deep learning pipeline to segment, but they are more representative of surgical conditions. Achieving a Dice Similarity Coefficient (DICE) score of 0.79, our model performs comparably to state-of-the-art segmentation models tested on noiseless data. This performance demonstrates the model's potential to assist surgeons in maximizing tumor resection and improving surgical outcomes. |
| title | Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2408.14847 |