Segment anything in medical images

Ma, Jun; He, Yuting; Li, Feifei; Han, Lin; You, Chenyu; Wang, Bo

doi:10.1038/s41467-024-44824-z

Cited by 167 publications

(37 citation statements)

References 31 publications

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“…To explore the preprocessing scheme’s assistance in improving accuracy, we desired a model that could adapt to medical images well. Therefore, in this research, we employed MedSAM [ 34 ], a SAM-based model specifically fine-tuned for medical images, as the testing model.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation

Kong,

Huang,

Zhang

et al. 2024

Bioengineering

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Medical imaging serves as a crucial tool in current cancer diagnosis. However, the quality of medical images is often compromised to minimize the potential risks associated with patient image acquisition. Computer-aided diagnosis systems have made significant advancements in recent years. These systems utilize computer algorithms to identify abnormal features in medical images, assisting radiologists in improving diagnostic accuracy and achieving consistency in image and disease interpretation. Importantly, the quality of medical images, as the target data, determines the achievable level of performance by artificial intelligence algorithms. However, the pixel value range of medical images differs from that of the digital images typically processed via artificial intelligence algorithms, and blindly incorporating such data for training can result in suboptimal algorithm performance. In this study, we propose a medical image-enhancement scheme that integrates generic digital image processing and medical image processing modules. This scheme aims to enhance medical image data by endowing them with high-contrast and smooth characteristics. We conducted experimental testing to demonstrate the effectiveness of this scheme in improving the performance of a medical image segmentation algorithm.

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Section: Experiments and Resultsmentioning

confidence: 99%

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation

Kong,

Huang,

Zhang

et al. 2024

Bioengineering

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“…Fine-tuning involves optimising a pre-trained model (architecture+weights) with data specific to a particular use case. Ma et al [12] demonstrated that employing SAM in medical images can enhance performance, particularly when the number of training images is substantially increased. The fine-tuning process involves multiple epochs, where the model iterates over the entire dataset, computing the loss between predicted masks and ground truth masks for each batch and updating the model's parameters using backpropagation.…”

Section: A Fine Tune Sam Modelmentioning

confidence: 99%

Practical Object and Flow Structure Segmentation using Artificial Intelligence

KHOJASTEH,

de Water,

Westerweel

2024

Preprint

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This paper explores integrating artificial intelligence (AI) segmentation models, particularly the Segment Anything Model (SAM), into fluid mechanics experiments. SAM's architecture, comprising an image encoder, prompt encoder, and mask decoder, is investigated for its application in detecting and segmenting objects and flow structures. Additionally, we explore the integration of natural language prompts, such as BERT, to enhance SAM's performance in segmenting specific objects. Through case studies, we found that SAM is robust in object detection in fluid experiments. However, segmentations related to flow properties, such as scalar turbulence and bubbly flows, require fine-tuning. To facilitate the application, we have established a repository (\href{https://github.com/AliRKhojasteh/Flow_segmentation}{github.com/AliRKhojasteh/Flow\_segmentation}) where models and usage examples can be accessed.

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“…To enhance SAM's adaptability to downstream tasks, some researchers have employed parameter fine-tuning techniques to improve its performance. MedSAM [30] utilizes parameter-efficient fine-tuning (PEFT) to fine-tune the pre-trained SAM model, demonstrating excellent performance in medical image segmentation. SAM-Med 2D [14] introduces adapters into the encoder for fine-tuning, making it well-suited for the medical domain.…”

Section: Fine-tuning Modelsmentioning

confidence: 99%

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation

Liao,

Wang,

et al. 2024

Applied Sciences

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In Cone Beam Computed Tomography (CBCT) images, accurate tooth segmentation is crucial for oral health, providing essential guidance for dental procedures such as implant placement and difficult tooth extractions (impactions). However, due to the lack of a substantial amount of dental data and the complexity of tooth morphology in CBCT images, the task of tooth segmentation faces significant challenges. This may lead to issues such as overfitting and training instability in existing algorithms, resulting in poor model generalization. Ultimately, this may impact the accuracy of segmentation results and could even provide incorrect diagnostic and treatment information. In response to these challenges, we introduce PPA-SAM, an innovative dual-encoder segmentation network that merges the currently popular Segment Anything Model (SAM) with the 3D medical segmentation network, VNet. Through the use of adapters, we achieve parameter reuse and fine-tuning, enhancing the model’s adaptability to specific CBCT datasets. Simultaneously, we utilize a three-layer convolutional network as both a discriminator and a generator for adversarial training. The PPA-SAM model seamlessly integrates the high-precision segmentation performance of convolutional networks with the outstanding generalization capabilities of SAM models, achieving more accurate and robust three-dimensional tooth segmentation in CBCT images. Evaluation of a small CBCT dataset demonstrates that PPA-SAM outperforms other networks in terms of accuracy and robustness, providing a reliable and efficient solution for three-dimensional tooth segmentation in CBCT images. This research has a positive impact on the management of dentofacial conditions from oral implantology to orthognathic surgery, offering dependable technological support for future oral diagnostics and treatment planning.

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Segment anything in medical images

Cited by 167 publications

References 31 publications

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation

Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation

Practical Object and Flow Structure Segmentation using Artificial Intelligence

PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation

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