Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection

• URL: http://arxiv.org/abs/2408.14847v1
• Date: Tue, 27 Aug 2024 07:58:08 GMT
• Title: Intraoperative Glioma Segmentation with YOLO + SAM for Improved Accuracy in Tumor Resection
• Authors: Samir Kassam, Angelo Markham, Katie Vo, Yashas Revanakara, Michael Lam, Kevin Zhu,
• Abstract summary: Gliomas present significant surgical challenges due to similarity to healthy tissue. MRI images are often ineffective during surgery due to factors such as brain shift. This paper presents a deep learning pipeline combining You Only Look Once Version 8 (Yv8) and Segment Anything Model Vision Transformer-base.
• Score: 1.9461727843485295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Style transfer between Microscopy and Magnetic Resonance Imaging via Generative Adversarial Network in small sample size settings [49.84018914962972]
  Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising. We tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture.
  arXiv  Detail & Related papers  (2023-10-16T13:58:53Z)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn) [9.082208613256295]
  We present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided.
  arXiv  Detail & Related papers  (2023-05-15T20:49:58Z)
• Brain Tumor Segmentation from MRI Images using Deep Learning Techniques [3.1498833540989413]
  A public MRI dataset contains 3064 TI-weighted images from 233 patients with three variants of brain tumor, viz. meningioma, glioma, and pituitary tumor. The dataset files were converted and preprocessed before indulging into the methodology which employs implementation and training of some well-known image segmentation deep learning models. The experimental findings showed that among all the applied approaches, the recurrent residual U-Net which uses Adam reaches a Mean Intersection Over Union of 0.8665 and outperforms other compared state-of-the-art deep learning models.
  arXiv  Detail & Related papers  (2023-04-29T13:33:21Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction [0.0]
  We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats 2020 Brain Tumor dataset. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set.
  arXiv  Detail & Related papers  (2022-04-04T01:45:11Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities [2.867517731896504]
  Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. It's common to miss some imaging modalities in clinical practice. We present a novel brain tumor segmentation algorithm with missing modalities.
  arXiv  Detail & Related papers  (2021-04-13T14:21:09Z)
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [47.26574993639482]
  We show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales.
  arXiv  Detail & Related papers  (2020-06-23T09:20:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.