Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation

• URL: http://arxiv.org/abs/2403.05433v1
• Date: Fri, 8 Mar 2024 16:34:30 GMT
• Title: Part-aware Personalized Segment Anything Model for Patient-Specific Segmentation
• Authors: Chenhui Zhao and Liyue Shen
• Abstract summary: Precision medicine, such as patient-adaptive treatments utilizing medical images, poses new challenges for image segmentation algorithms. We propose a data-efficient segmentation method to address these challenges, namely Part-aware Personalized Segment Anything Model (P2SAM) We introduce a novel part-aware prompt mechanism to select multiple-point prompts based on part-level features of the one-shot data.
• Score: 5.797437925674252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precision medicine, such as patient-adaptive treatments utilizing medical images, poses new challenges for image segmentation algorithms due to (1) the large variability across different patients and (2) the limited availability of annotated data for each patient. In this work, we propose a data-efficient segmentation method to address these challenges, namely Part-aware Personalized Segment Anything Model (P^2SAM). Without any model fine-tuning, P^2SAM enables seamless adaptation to any new patients relying only on one-shot patient-specific data. We introduce a novel part-aware prompt mechanism to select multiple-point prompts based on part-level features of the one-shot data. To further promote the robustness of the selected prompt, we propose a retrieval approach to handle outlier prompts. Extensive experiments demonstrate that P^2SAM improves the performance by +8.0% and +2.0% mean Dice score within two patient-specific segmentation settings, and exhibits impressive generality across different application domains, e.g., +6.4% mIoU on the PerSeg benchmark. Code will be released upon acceptance.

Related papers

• Retrieval-augmented Few-shot Medical Image Segmentation with Foundation Models [17.461510586128874]
  We propose a novel method that adapts DINOv2 and Segment Anything Model 2 for retrieval-augmented few-shot medical image segmentation. Our approach uses DINOv2's feature as query to retrieve similar samples from limited annotated data, which are then encoded as memories and stored in memory bank.
  arXiv  Detail & Related papers  (2024-08-16T15:48:07Z)
• Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation [44.54301473673582]
  Semi-supervised learning (SSL) has achieved notable progress in medical image segmentation. Recent developments in visual foundation models, such as the Segment Anything Model (SAM), have demonstrated remarkable adaptability. We propose a cross-prompting consistency method with segment anything model (CPC-SAM) for semi-supervised medical image segmentation.
  arXiv  Detail & Related papers  (2024-07-07T15:43:20Z)
• ASPS: Augmented Segment Anything Model for Polyp Segmentation [77.25557224490075]
  The Segment Anything Model (SAM) has introduced unprecedented potential for polyp segmentation. SAM's Transformer-based structure prioritizes global and low-frequency information. CFA integrates a trainable CNN encoder branch with a frozen ViT encoder, enabling the integration of domain-specific knowledge.
  arXiv  Detail & Related papers  (2024-06-30T14:55:32Z)
• Uncertainty-Aware Adapter: Adapting Segment Anything Model (SAM) for Ambiguous Medical Image Segmentation [20.557472889654758]
  The Segment Anything Model (SAM) gained significant success in natural image segmentation. Unlike natural images, many tissues and lesions in medical images have blurry boundaries and may be ambiguous. We propose a novel module called the Uncertainty-aware Adapter, which efficiently fine-tune SAM for uncertainty-aware medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-16T14:11:54Z)
• 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)
• Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data [16.356954231068077]
  Early detection of colorectal polyps is of utmost importance for their treatment and for colorectal cancer prevention. Computer vision techniques have the potential to aid professionals in the diagnosis stage, where colonoscopies are manually carried out to examine the entirety of the patient's colon. The main challenge in medical imaging is the lack of data, and a further challenge specific to polyp segmentation approaches is the difficulty of manually labeling the available data. We propose an end-to-end model for polyp segmentation that integrates real and synthetic data to artificially increase the size of the datasets and aid the training when unlabeled samples are available.
  arXiv  Detail & Related papers  (2023-07-22T09:57:58Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• A Meta-GNN approach to personalized seizure detection and classification [53.906130332172324]
  We propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We train a Meta-GNN based classifier that learns a global model from a set of training patients. We show that our method outperforms the baselines by reaching 82.7% on accuracy and 82.08% on F1 score after only 20 iterations on new unseen patients.
  arXiv  Detail & Related papers  (2022-11-01T14:12:58Z)
• Adversarial Sample Enhanced Domain Adaptation: A Case Study on Predictive Modeling with Electronic Health Records [57.75125067744978]
  We propose a data augmentation method to facilitate domain adaptation. adversarially generated samples are used during domain adaptation. Results confirm the effectiveness of our method and the generality on different tasks.
  arXiv  Detail & Related papers  (2021-01-13T03:20:20Z)
• DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
  We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation. Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives. To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
  arXiv  Detail & Related papers  (2020-08-19T06:02:46Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)

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.