Polyp-DAM: Polyp Segmentation via Depth Anything Model

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-09-16 DOI:10.1109/LSP.2024.3461654
Zhuoran Zheng;Chen Wu;Yeying Jin;Xiuyi Jia
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Abstract

Recently, large models (Segment Anything model) came on the scene to provide a new baseline for polyp segmentation tasks. This demonstrates that large models with a sufficient image level prior can achieve promising performance on a given task. In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models. Specifically, the input polyp image is first passed through a frozen DAM to generate a depth map. The depth map and the input polyp images are then concatenated and fed into a convolutional neural network with multiscale to generate segmented images. Extensive experimental results demonstrate the effectiveness of our method, and in addition, we observe that our method still performs well on images of polyps with noise.
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Polyp-DAM:通过深度任意模型进行多边形分割
最近,大型模型(Segment Anything model)的出现为息肉分割任务提供了新的基准。这表明,具有足够图像级先验的大型模型可以在给定任务中取得可喜的性能。在本文中,我们利用深度任意模型(DAM)为息肉分割模型提供深度先验,从而为息肉分割建模提供了一个新的视角。具体来说,输入的息肉图像首先通过冻结的 DAM 生成深度图。然后将深度图和输入的息肉图像连接起来,并输入具有多尺度的卷积神经网络,生成分割图像。广泛的实验结果证明了我们方法的有效性,此外,我们还观察到我们的方法在有噪声的息肉图像上仍然表现良好。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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