Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan

• URL: http://arxiv.org/abs/2504.04066v1
• Date: Sat, 05 Apr 2025 05:47:56 GMT
• Title: Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan
• Authors: Mengyuan Liu, Yixiao Chen, Anning Tian, Xinmeng Wu, Mozhi Shen, Tianchou Gong, Jeongkyu Lee,
• Abstract summary: 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.
• Score: 5.5193366921929155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks like U-Net excel in medical image segmentation, while attention mechanisms and KAN enhance feature extraction. Meta's SAM 2 uses Vision Transformers for prompt-based segmentation without fine-tuning. However, biases in these models impact generalization with limited data. In this study, we systematically 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, i.e., SAM 2 for segmenting femur bone structures in MRI scan. The dataset comprises 11,164 MRI scans with detailed annotations of femoral regions. Performance is assessed using the Dice Similarity Coefficient, which ranges from 0.932 to 0.954. Attention U-Net achieves the highest overall scores, while U-KAN demonstrated superior performance in anatomical regions with a smaller region of interest, leveraging its enhanced learning capacity to improve segmentation accuracy.

Related papers

• State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters [1.581945821289601]
  We introduce a novel multi-scale dilation pattern with an encoder-decoder structure. We operate directly on whole-brain $2563$ MRI volumes. Our results validate MeshNet's strong balance of efficiency and performance.
  arXiv  Detail & Related papers  (2025-03-07T15:58:36Z)
• SAMRI-2: A Memory-based Model for Cartilage and Meniscus Segmentation in 3D MRIs of the Knee Joint [0.7879983966759583]
  This study introduces a deep learning (DL) method for cartilage and meniscus segmentation from 3D MRIs using memory-based VFMs.<n>We trained four AI models-a CNN-based 3D-VNet, two automatic transformer-based models (SaMRI2D and SaMRI3D), and a transformer-based promptable memory-based VFM (SAMRI-2)-on 3D knee MRIs from 270 patients.<n>SAMRI-2 model, trained with HSS, outperformed all other models, achieving an average improvement of 5 points, with a peak improvement of 12 points for tibial cartilage.
  arXiv  Detail & Related papers  (2025-02-14T21:18:01Z)
• Y-CA-Net: A Convolutional Attention Based Network for Volumetric Medical Image Segmentation [47.12719953712902]
  discriminative local features are key components for the performance of attention-based VS methods. We incorporate the convolutional encoder branch with transformer backbone to extract local and global features in a parallel manner. Y-CT-Net achieves competitive performance on multiple medical segmentation tasks.
  arXiv  Detail & Related papers  (2024-10-01T18:50:45Z)
• 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)
• 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)
• 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)
• FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures [4.507779218329283]
  Segmenting retinal vessels presents unique challenges.<n>Recent neural network approaches struggle to balance local and global properties.<n>We propose a comprehensive micro-vessel extraction mechanism based on an encoder-decoder neural network architecture.
  arXiv  Detail & Related papers  (2023-11-14T10:32:17Z)
• nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance [12.169801149021566]
  The Segment Anything Model (SAM) has emerged as a versatile tool for image segmentation without specific domain training. Traditional models like nnUNet perform automatic segmentation during inference but need extensive domain-specific training. We propose nnSAM, integrating SAM's robust feature extraction with nnUNet's automatic configuration to enhance segmentation accuracy on small datasets.
  arXiv  Detail & Related papers  (2023-09-29T04:26:25Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation [52.77024349608834]
  This paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results. The best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively.
  arXiv  Detail & Related papers  (2022-10-19T15:15:32Z)
• CNN-based fully automatic wrist cartilage volume quantification in MR Image [55.41644538483948]
  The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
  arXiv  Detail & Related papers  (2022-06-22T14:19:06Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.