Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter

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PURPOSE: In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves competitive performance with training on limited datasets, while also enhancing model robustness in various surgical scenarios.

METHODS: We propose a method that harnesses the strengths of pre-trained Vision Transformers (ViTs) and data efficiency of convolutional neural networks (CNNs). Specifically, we demonstrate how a CNN segmentation model can be used as a lightweight adapter for a frozen ViT feature encoder. Our novel feature adapter uses cross-attention modules that merge the multiscale features derived from the CNN encoder with feature embeddings from ViT, ensuring integration of the global insights from ViT along with local information from CNN.

RESULTS: Extensive experiments demonstrate our method outperforms current models in surgical instrument segmentation. Specifically, it achieves superior performance in binary segmentation on the Robust-MIS 2019 dataset, as well as in multiclass segmentation tasks on the EndoVis 2017 and EndoVis 2018 datasets. It also showcases remarkable robustness through cross-dataset validation across these 3 datasets, along with the CholecSeg8k and AutoLaparo datasets. Ablation studies based on the datasets prove the efficacy of our novel adapter module.

CONCLUSION: In this study, we presented a novel approach integrating ViT and CNN. Our unique feature adapter successfully combines the global insights of ViT with the local, multi-scale spatial capabilities of CNN. This integration effectively overcomes data limitations in surgical instrument segmentation. The source code is available at: https://github.com/weimengmeng1999/AdapterSIS.git .

Original languageEnglish
JournalInternational Journal of Computer Assisted Radiology and Surgery
Early online date8 May 2024
Publication statusE-pub ahead of print - 8 May 2024


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