PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

Li, Changyong; Fan, Yongxian; Cai, Xiaodong

doi:10.1186/s12859-020-03943-2

Cited by 16 publications

(8 citation statements)

References 18 publications

(22 reference statements)

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“…Moreover, Tarasiewicz et al [47] developed Lightweight U-Nets that can accurately delineate brain tumors from multimodal MRIs and trained several skinny networks across all image planes. PyConvU-Net [15] increases segmentation accuracy while using fewer parameters by replacing all traditional U-Net's convolutional layers with pyramidal convolution. Nonetheless, its inference speed is low.…”

Section: Related Workmentioning

confidence: 99%

“…In this domain, widely recognized encoders, including VGG and ResNet, are renowned for their exceptional feature extraction capabilities [8]- [10]. Additionally, other techniques such as multiscale pooling [11], dilated convolution [12], and attention mechanisms [13]- [15], are employed to extract semantic information from medical images. Additionally, U-Net is a widely used backbone architecture for medical image segmentation due to its efficient skip connections that augment low-level features [6], [16], [17].…”

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confidence: 99%

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MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Iqbal

Sharif

2022

Journal of King Saud University - Computer and Information Scie

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Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

Iqbal

Sharif

2022

Journal of King Saud University - Computer and Information Scie

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“…Recently, Duta et al [19] proposed the pyramidal convolution for visual recognition, which can then be used for other tasks, such as semantic segmentation [20] and object detection [21]. From this perspective of multi-scale feature extraction, Zhang et al [22] proposed the lightweight segmentation algorithm based on multi-scale pyramidal convolution, which is dubbed PylNet.…”

Section: Bladder Tumor Segmentationmentioning

confidence: 99%

MSEDTNet: Multi-Scale Encoder and Decoder with Transformer for Bladder Tumor Segmentation

Wang

2022

Electronics

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The precise segmentation of bladder tumors from MRI is essential for bladder cancer diagnosis and personalized therapy selection. Limited by the properties of tumor morphology, achieving precise segmentation from MRI images remains challenging. In recent years, deep convolutional neural networks have provided a promising solution for bladder tumor segmentation from MRI. However, deep-learning-based methods still face two weakness: (1) multi-scale feature extraction and utilization are inadequate, being limited by the learning approach. (2) The establishment of explicit long-distance dependence is difficult due to the limited receptive field of convolution kernels. These limitations raise challenges in the learning of global semantic information, which is critical for bladder cancer segmentation. To tackle the problem, a newly auxiliary segmentation algorithm integrating a multi-scale encoder and decoder with a transformer is proposed, which is called MSEDTNet. Specifically, the designed encoder with multi-scale pyramidal convolution (MSPC) is utilized to generate compact feature maps which capture the richly detailed local features of the image. Furthermore, the transformer bottleneck is then leveraged to model the long-distance dependency between high-level tumor semantics from a global space. Finally, a decoder with a spatial context fusion module (SCFM) is adopted to fuse the context information and gradually produce high-resolution segmentation results. The experimental results of T2-weighted MRI scans from 86 patients show that MSEDTNet achieves an overall Jaccard index of 83.46%, a Dice similarity coefficient of 92.35%, and a complexity less than that of other, similar models. This suggests that the method proposed in this article can be used as an efficient tool for clinical bladder cancer segmentation.

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“…The lightweight multiscale network named PyconvUNet proposed by Li et al introduces pyramidal convolution to replace the convolutional layers of traditional codec networks. This method effectively reduces the number of channels in the image and achieves low-resource computation (Li et al 2021a). The LightUNet proposed by Li et al introduces group convolution into the UNet and provides insights into the effect of different group sizes on the performance of the network.…”

Section: Introductionmentioning

confidence: 99%

MAUNext: a lightweight segmentation network for medical images

Wang,

Zhou

et al. 2023

Phys. Med. Biol.

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Objective. The primary objective of this study is to enhance medical image segmentation techniques for clinical research by prioritizing accuracy and the number of parameters. Approach. To achieve this objective, a novel codec-based MAUNext approach is devised, focusing on lightweight backbone design and the integration of skip connections utilizing multiscale, attention mechanism, and other strategic components. The approach is composed of three core modules: a multi-scale attentional convolution module for improved accuracy and parameter reduction, a collaborative neighbourhood-attention MLP encoding module to enhance segmentation performance, and a tiny skip-connected cross-layer semantic fusion module to bridge the semantic gap between encoder and decoder. Main results. The study extensively evaluates the MAUNext approach alongside eight state-of-the-art methods on three renowned datasets: Kagglelung, ISIC, and Brain. The experimental outcomes robustly demonstrate that the proposed approach surpasses other methods in terms of both parameter numbers and accuracy. This achievement holds promise for effectively addressing medical image segmentation tasks. Significance. Automated medical image segmentation, particularly in organ and lesion identification, plays a pivotal role in clinical diagnosis and treatment. Manual segmentation is resource-intensive, thus automated methods are highly valuable. The study underscores the clinical significance of automated segmentation by providing an advanced solution through the innovative MAUNext approach. This approach offers substantial improvements in accuracy and efficiency, which can significantly aid clinical decision-making and patient treatment.

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PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

Cited by 16 publications

References 18 publications

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

MDA-Net: Multiscale dual attention-based network for breast lesion segmentation using ultrasound images

MSEDTNet: Multi-Scale Encoder and Decoder with Transformer for Bladder Tumor Segmentation

MAUNext: a lightweight segmentation network for medical images

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