AI-Driven MRI-based Brain Tumour Segmentation Benchmarking

• URL: http://arxiv.org/abs/2506.20786v1
• Date: Wed, 25 Jun 2025 19:18:26 GMT
• Title: AI-Driven MRI-based Brain Tumour Segmentation Benchmarking
• Authors: Connor Ludwig, Khashayar Namdar, Farzad Khalvati,
• Abstract summary: This research uses pediatrics Segment Anything Model (SAM), Segment Anything Model 2 (SAM 2), MedSAM, SAM-Med-3D, and nnU-Net to obtain zero-shot inference on the BraTS 2023 adult glioma and pediatrics dataset.<n>Several models exhibit promising Dice scores, particularly SAM and SAM 2 achieving scores of up to 0.894 and 0.893, respectively when given extremely accurate bounding box prompts.<n>However, nnU-Net remains the dominant medical image segmentation network due to the impracticality of providing highly accurate prompts to the models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical image segmentation has greatly aided medical diagnosis, with U-Net based architectures and nnU-Net providing state-of-the-art performance. There have been numerous general promptable models and medical variations introduced in recent years, but there is currently a lack of evaluation and comparison of these models across a variety of prompt qualities on a common medical dataset. This research uses Segment Anything Model (SAM), Segment Anything Model 2 (SAM 2), MedSAM, SAM-Med-3D, and nnU-Net to obtain zero-shot inference on the BraTS 2023 adult glioma and pediatrics dataset across multiple prompt qualities for both points and bounding boxes. Several of these models exhibit promising Dice scores, particularly SAM and SAM 2 achieving scores of up to 0.894 and 0.893, respectively when given extremely accurate bounding box prompts which exceeds nnU-Net's segmentation performance. However, nnU-Net remains the dominant medical image segmentation network due to the impracticality of providing highly accurate prompts to the models. The model and prompt evaluation, as well as the comparison, are extended through fine-tuning SAM, SAM 2, MedSAM, and SAM-Med-3D on the pediatrics dataset. The improvements in point prompt performance after fine-tuning are substantial and show promise for future investigation, but are unable to achieve better segmentation than bounding boxes or nnU-Net.

Related papers

• Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan [5.5193366921929155]
  We evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, SAM 2.<n>The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions.<n> Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest.
  arXiv  Detail & Related papers  (2025-04-05T05:47:56Z)
• Learnable Prompting SAM-induced Knowledge Distillation for Semi-supervised Medical Image Segmentation [47.789013598970925]
  We propose a learnable prompting SAM-induced Knowledge distillation framework (KnowSAM) for semi-supervised medical image segmentation.<n>Our model outperforms the state-of-the-art semi-supervised segmentation approaches.
  arXiv  Detail & Related papers  (2024-12-18T11:19:23Z)
• DB-SAM: Delving into High Quality Universal Medical Image Segmentation [100.63434169944853]
  We propose a dual-branch adapted SAM framework, named DB-SAM, to bridge the gap between natural and 2D/3D medical data. Our proposed DB-SAM achieves an absolute gain of 8.8%, compared to a recent medical SAM adapter in the literature.
  arXiv  Detail & Related papers  (2024-10-05T14:36:43Z)
• PAM: A Propagation-Based Model for Segmenting Any 3D Objects across Multi-Modal Medical Images [11.373941923130305]
  PAM (Propagating Anything Model) is a segmentation approach that uses a 2D prompt, like a bounding box or sketch, to create a complete 3D segmentation of medical image volumes. It significantly outperformed existing models like MedSAM and SegVol, with an average improvement of over 18.1% in dice similarity coefficient (DSC) across 44 medical datasets and various object types.
  arXiv  Detail & Related papers  (2024-08-25T13:42:47Z)
• Improved Baselines with Synchronized Encoding for Universal Medical Image Segmentation [34.08601740109437]
  We introduce SyncSAM, which employs a synchronized dual-branch encoder that integrates convolution and Transformer features in a synchronized manner to enhance medical image encoding.<n>SyncSAM achieves state-of-the-art performance on test sets and also exhibits strong zero-shot capabilities on unseen datasets.
  arXiv  Detail & Related papers  (2024-08-19T11:01:00Z)
• Novel adaptation of video segmentation to 3D MRI: efficient zero-shot knee segmentation with SAM2 [1.6237741047782823]
  We introduce a method for zero-shot, single-prompt segmentation of 3D knee MRI by adapting Segment Anything Model 2. By treating slices from 3D medical volumes as individual video frames, we leverage SAM2's advanced capabilities to generate motion- and spatially-aware predictions. We demonstrate that SAM2 can efficiently perform segmentation tasks in a zero-shot manner with no additional training or fine-tuning.
  arXiv  Detail & Related papers  (2024-08-08T21:39:15Z)
• Is SAM 2 Better than SAM in Medical Image Segmentation? [0.6144680854063939]
  The Segment Anything Model (SAM) has demonstrated impressive performance in zero-shot promptable segmentation on natural images. The recently released Segment Anything Model 2 (SAM 2) claims to outperform SAM on images and extends the model's capabilities to video segmentation. We conducted extensive studies using multiple datasets to compare the performance of SAM and SAM 2.
  arXiv  Detail & Related papers  (2024-08-08T04:34:29Z)
• TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI [59.86827659781022]
  A nnU-Net model (TotalSegmentator) was trained on MRI and segment 80atomic structures.<n>Dice scores were calculated between the predicted segmentations and expert reference standard segmentations to evaluate model performance.<n>Open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures.
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• Comprehensive Multimodal Segmentation in Medical Imaging: Combining YOLOv8 with SAM and HQ-SAM Models [0.24578723416255752]
  The proposed method harnesses the capabilities of the YOLOv8 model for approximate boundary box detection across modalities. To generate boundary boxes, the YOLOv8 model was trained using a limited set of 100 images and masks from each modality. A comparative analysis was conducted to assess the individual and combined performance of the YOLOv8, YOLOv8+SAM, and YOLOv8+HQ-SAM models.
  arXiv  Detail & Related papers  (2023-10-04T20:30:49Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation [51.770805270588625]
  The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation. Recent studies and individual experiments have shown that SAM underperforms in medical image segmentation. We propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model.
  arXiv  Detail & Related papers  (2023-04-25T07:34:22Z)
• Segment Anything Model for Medical Image Analysis: an Experimental Study [19.95972201734614]
  Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. We evaluate SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies.
  arXiv  Detail & Related papers  (2023-04-20T17:50:18Z)

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.