MRDFF: A deep forest based framework for CT whole heart segmentation

Xu, Fei; Lin, Lingli; Hong, Qingqi; Liu, Kun-Hong; Wu, Qingqiang; Li, Qingde; Zheng, Yuliang; Tian, Jie

doi:10.1016/j.ymeth.2022.10.005

Cited by 8 publications

(1 citation statement)

References 21 publications

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“…However, the pseudo labels generated from CAMs for training a WSSS model have an issue of partial activation, which generally tends to highlight the most discriminative part of an object instead of the entire object area [19], [20]. Recent works [21], [22] have pointed out that the reason may be the intrinsic characteristic of CNNs, i.e., the local receptive field only captures smallrange feature dependencies. Although various methods have been proposed to identify an activation area aligned with the entire object region [19], [20], [23], little work has directly addressed the local receptive field deficiencies of the CNN when applied to WSSS.…”

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confidence: 99%

ScribFormer: Transformer Makes CNN Work Better for Scribble-Based Medical Image Segmentation

Li,

Zheng,

Shan

et al. 2024

IEEE Trans. Med. Imaging

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Most recent scribble-supervised segmentation methods commonly adopt a CNN framework with an encoder-decoder architecture. Despite its multiple benefits, this framework generally can only capture small-range feature dependency for the convolutional layer with the local receptive field, which makes it difficult to learn global shape information from the limited information provided by scribble annotations. To address this issue, this paper proposes a new CNN-Transformer hybrid solution for scribble-supervised medical image segmentation called ScribFormer. The proposed ScribFormer model has a triplebranch structure, i.e., the hybrid of a CNN branch, a Transformer branch, and an attention-guided class activation map (ACAM) branch. Specifically, the CNN branch collaborates with the Transformer branch to fuse the local features learned from CNN with the global representations obtained from Transformer, which can effectively overcome limitations of existing scribble-supervised segmentation methods. Furthermore, the ACAM branch assists in unifying the shallow convolution features and the deep convolution features to further improve model's performance. Extensive experiments on two public datasets and one private dataset show that our ScribFormer has superior performance over the state-of-the-art scribble-supervised segmentation methods, and achieves even better results than the fullysupervised segmentation methods. The code is released at https://github.com/HUANGLIZI/ScribFormer.

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