A novel DAVnet3+ method for precise segmentation of bladder cancer in MRI

Wang, Liang; Cai, Lingkai; Chen, Chunxiao; Fu, Xue; Yu, Jie; Ge, Rongjun; Yuan, Baorui; Yang, Xiao; Shao, Qiang; Lv, Qiang

doi:10.1007/s00371-022-02622-y

Cited by 4 publications

(1 citation statement)

References 33 publications

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“…The CPA-UNet achieves superior segmentation results regarding the Dice similarity coefficient of 87.40%. In addition, Wang et al [30] proposed a dual-attention Vnet triple plus (DAVnet3+) network to accurately segment tumor regions. They used full-scale skip connections to fully utilize the information of multiscale feature maps.…”

Section: Introductionmentioning

confidence: 99%

LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images

Wang,

Li,

2023

Mathematics

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Accurate segmentation of the lesion area from MRI images is essential for diagnosing bladder cancer. However, the precise segmentation of bladder tumors remains a massive challenge due to their similar intensity distributions, various tumor morphologies, and blurred boundaries. While some seminal studies, such as those using CNNs combined with transformer segmentation methods, have made significant progress, (1) how to reduce the computational complexity of the self-attention mechanism in the transformer while maintaining performance and (2) how to build a better global feature fusion process to improve segmentation performance still require further exploration. Considering the complexity of bladder MRI images, we developed a lightweight context-aware network (LCANet) to automatically segment bladder lesions from MRI images. Specifically, the local detail encoder generates local-level details of the lesion, the lightweight transformer encoder models the global-level features with different resolutions, the pyramid scene parsing module extracts high-level and multiscale semantic features, and the decoder provides high-resolution segmentation results by fusing local-level details with global-level cues at the channel level. A series of empirical studies on T2-weighted MRI images from 86 patients show that LCANet achieves an overall Jaccard index of 89.39%, a Dice similarity coefficient of 94.08%, and a Class pixel accuracy of 94.10%. These advantages show that our method is an efficient tool that can assist in reducing the heavy workload of radiologists.

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Section: Introductionmentioning

confidence: 99%

LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images

Wang,

Li,

2023

Mathematics

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3D reconstruction-oriented fully automatic multi-modal tumor segmentation by dual attention-guided VNet

Meng

Sheng

et al. 2023

Vis Comput

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Boundary guidance network for medical image segmentation

Xu,

et al. 2024

Sci Rep

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Accurate segmentation of the tumor area is crucial for the treatment and prognosis of patients with bladder cancer. Cystoscopy is the gold standard for diagnosing bladder tumors. However, The vast majority of current work uses deep learning to identify and segment tumors from CT and MRI findings, and rarely involves cystoscopy findings. Accurately segmenting bladder tumors remains a great challenge due to their diverse morphology and fuzzy boundaries. In order to solve the above problems, this paper proposes a medical image segmentation network with boundary guidance called boundary guidance network. This network combines local features extracted by CNNs and long-range dependencies between different levels inscribed by Parallel ViT, which can capture tumor features more effectively. The Boundary extracted module is designed to extract boundary features and utilize the boundary features to guide the decoding process. Foreground-background dual-channel decoding is performed by boundary integrated module. Experimental results on our proposed new cystoscopic bladder tumor dataset (BTD) show that our method can efficiently perform accurate segmentation of tumors and retain more boundary information, achieving an IoU score of 91.3%, a Hausdorff Distance of 10.43, an mAP score of 85.3%, and a F1 score of 94.8%. On BTD and three other public datasets, our model achieves the best scores compared to state-of-the-art methods, which proves the effectiveness of our model for common medical image segmentation.

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A novel DAVnet3+ method for precise segmentation of bladder cancer in MRI

Cited by 4 publications

References 33 publications

LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images

LCANet: A Lightweight Context-Aware Network for Bladder Tumor Segmentation in MRI Images

3D reconstruction-oriented fully automatic multi-modal tumor segmentation by dual attention-guided VNet

Boundary guidance network for medical image segmentation

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