Latest advances in segmentation basis fashions just like the Phase Something Mannequin (SAM) have proven spectacular efficiency on pure photographs and movies. Nonetheless, their utility to medical knowledge stays to be decided. SAM, educated on an enormous dataset of pure photographs, struggles with medical photographs as a consequence of area variations like decrease decision and distinctive picture challenges. Though MedSAM has improved 2D medical picture segmentation by fine-tuning, its efficiency on 3D footage and movies is proscribed. SAM2 extends SAM to video segmentation and reveals promise, however its effectiveness on medical knowledge, significantly 3D pictures and movies, has but to be totally evaluated.
Researchers from the College Well being Community and the College of Toronto have comprehensively evaluated the Phase Something Mannequin 2 (SAM2) throughout 11 medical picture modalities and movies. They in contrast SAM2 with SAM1 and MedSAM, figuring out each strengths and weaknesses. They developed a switch studying pipeline to adapt SAM2 for medical use and efficiently fine-tuned the mannequin. Moreover, they built-in SAM2 right into a 3D Slicer plugin. They carried out a Gradio API, enabling environment friendly 3D picture and video segmentation for medical knowledge like CT, MR, and PET, which the official SAM2 interface doesn’t help.
The examine used public datasets from the CVPR 2024 Medical Picture Segmentation on Laptop computer Problem for analysis, excluding any knowledge from the MedSAM coaching set. CT photographs had been preprocessed with depth cutoffs, MR and PET photographs had been clipped and normalized, whereas different modalities remained unchanged. All photographs had been transformed to npz format for batch inference. SAM2, an extension of SAM1, incorporates Hiera for multi-scale characteristic extraction and a reminiscence consideration module for constant video segmentation throughout frames. The fine-tuning of SAM2-Tiny concerned freezing the immediate encoder, updating the picture encoder and masks decoder, and utilizing Cube and cross-entropy losses for strong segmentation.
The benchmark dataset used within the examine consists of 11 generally used medical picture modalities, akin to CT, MRI, PET, and ultrasound. SAM2, a flexible picture and video segmentation mannequin, was evaluated on 2D, 3D, and video datasets. Comparisons had been made with SAM1 and MedSAM throughout numerous mannequin sizes. The analysis concerned segmenting 2D photographs straight, whereas 3D photographs had been handled as sequences of 2D slices, with segmentation masks propagated from the center slice. SAM2’s video segmentation functionality allowed it to deal with dynamic object areas throughout frames, which is especially helpful for ultrasound and endoscopy movies.
The outcomes confirmed that SAM2 outperformed SAM1 in a number of modalities like MR and dermoscopy, however MedSAM constantly achieved higher ends in most 2D modalities aside from PET and light-weight microscopy. In 3D segmentation, SAM2 demonstrated vital enhancements over SAM1 and MedSAM in CT and MR photographs by leveraging its video segmentation capabilities. Nevertheless, SAM2 struggled with PET photographs as a consequence of over-segmentation errors. Switch studying was utilized to adapt SAM2 to medical domains, leading to substantial efficiency good points throughout numerous organs in 3D CT scans. To boost accessibility for medical professionals, user-friendly interfaces primarily based on 3D Slicer and Gradio had been developed for basic 3D medical picture and video segmentation.
In conclusion, the examine compares the efficiency of SAM2 and SAM1 fashions in medical picture segmentation, revealing that SAM2 outperforms SAM1 in sure 2D and 3D modalities, like MRI and CT, as a consequence of its superior structure and coaching on bigger datasets. Nevertheless, SAM1 performs higher in others, akin to OCT and PET. The evaluation reveals that greater than mannequin dimension is required, as smaller SAM2 variants typically excel. SAM2’s video segmentation capabilities improve its utility for 3D medical photographs however lag behind the specialised MedSAM in 2D duties. The paper additionally highlights the potential for switch studying to enhance SAM2’s medical picture segmentation efficiency.
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