Black-box Domain Adaptative Cell Segmentation via Multi-source Distillation

Wang, Xingguang; Li, Zhongyu; Luo, Xiangde; Wan, Jing; Zhu, Jianwei; Yang, Ziqi; Yang, Meng; Xu, Cunbao

doi:10.1007/978-3-031-43907-0_71

Cited by 1 publication

(1 citation statement)

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“…To alleviate the learning bias caused by artificially designed disturbances in semi-supervised models, Zhou et al (2023) proposed a consistency training method based on wavelet to address low-frequency and high-frequency information. Wang et al (2023) introduced a two-stage knowledge distillation method designed to prevent the accumulation of errors resulting from noise artifacts.…”

Section: Introductionmentioning

confidence: 99%

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

Zhang,

et al. 2024

Front. Neurosci.

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BackgroundAutomatic segmentation of corneal stromal cells can assist ophthalmologists to detect abnormal morphology in confocal microscopy images, thereby assessing the virus infection or conical mutation of corneas, and avoiding irreversible pathological damage. However, the corneal stromal cells often suffer from uneven illumination and disordered vascular occlusion, resulting in inaccurate segmentation.MethodsIn response to these challenges, this study proposes a novel approach: a nnUNet and nested Transformer-based network integrated with dual high-order channel attention, named U-NTCA. Unlike nnUNet, this architecture allows for the recursive transmission of crucial contextual features and direct interaction of features across layers to improve the accuracy of cell recognition in low-quality regions. The proposed methodology involves multiple steps. Firstly, three underlying features with the same channel number are sent into an attention channel named gnConv to facilitate higher-order interaction of local context. Secondly, we leverage different layers in U-Net to integrate Transformer nested with gnConv, and concatenate multiple Transformers to transmit multi-scale features in a bottom-up manner. We encode the downsampling features, corresponding upsampling features, and low-level feature information transmitted from lower layers to model potential correlations between features of varying sizes and resolutions. These multi-scale features play a pivotal role in refining the position information and morphological details of the current layer through recursive transmission.ResultsExperimental results on a clinical dataset including 136 images show that the proposed method achieves competitive performance with a Dice score of 82.72% and an AUC (Area Under Curve) of 90.92%, which are higher than the performance of nnUNet.ConclusionThe experimental results indicate that our model provides a cost-effective and high-precision segmentation solution for corneal stromal cells, particularly in challenging image scenarios.

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Section: Introductionmentioning

confidence: 99%

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

Zhang,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

Black-box Domain Adaptative Cell Segmentation via Multi-source Distillation

Cited by 1 publication

References 15 publications

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

U-NTCA: nnUNet and nested transformer with channel attention for corneal cell segmentation

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