Pancreas segmentation using a dual-input v-mesh network

Wang, Yuan; Gong, Guanzhong; Kong, Deting; Li, Qi; Dai, Jinpeng; Zhang, Hongyan; Qu, Jianhua; Liu, Xiyu; Xue, Jie

doi:10.1016/j.media.2021.101958

Cited by 27 publications

(16 citation statements)

References 24 publications

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“…We first test our model (as well as its lightweight variant) on the NIH Pancreas-CT dataset and compare it to existing methods (which share our evaluation strategy with 4-fold cross-validation), namely, [2,8,10,11,12,15,16,17,21,22,24,25]. Summarized in Table 1, our results indicate that PanKNet outperforms existing methods over different metrics.…”

Section: Resultsmentioning

confidence: 96%

“…Summarized in Table 1, our results indicate that PanKNet outperforms existing methods over different metrics. Note that PanKNet does not require any auxiliary regularization networks [8], nor additional inputs [22], nor upstream pancreas localization module [12] even the lightweight variant of PanKNet yields accuracy comparable to the full model, while outperforming existing models, showing that the choice of the backbone is not as important as the overall employed hierarchical architecture. The best trade-off between accuracy and computational resources for CT pancreas segmentation is represented by PanKNet Light , whose memory occupation is about 10 MB compared to about 100 MB of PanKNet, but with very similar performance.…”

Section: Resultsmentioning

confidence: 99%

“…Following the success of deep learning methods applied in medical image segmentation, researchers have recently shown an increasing interest in pancreas segmentation, in order to support physicians in early stage diagnosis for pancreas cancer. Although this application field is still in its infancy -also due to variabilities in texture, size and imaging contrast -a line of promising approaches has been proposed in the literature, mainly on CT scans [2,8,10,11,12,15,16,17,21,22,24,25]. We here describe the most significant ones which relate to our proposed model.…”

Section: Related Workmentioning

confidence: 99%

“…Projective adversarial networks [8] incorporate high-level 3D information through 2D projections and introduce an attention module that supports a selective integration of global information from the segmentation module to an adversarial network. More recently, [22] proposes a dual-input v-mesh fully-convolutional network, which receives original CT scans and images processed by contrast-specific graph-based visual saliency, in order to enhance the soft tissue contrast and highlight differences among local regions in abdominal CT scans. All of the above works tackle the problem of pancreas segmentation on CT scans.…”

Section: Related Workmentioning

confidence: 99%

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Hierarchical 3D Feature Learning for Pancreas Segmentation

Salanitri,

Bellitto,

Irmakci

et al. 2021

Preprint

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We propose a novel 3D fully convolutional deep network for automated pancreas segmentation from both MRI and CT scans. More specifically, the proposed model consists of a 3D encoder that learns to extract volume features at different scales; features taken at different points of the encoder hierarchy are then sent to multiple 3D decoders that individually predict intermediate segmentation maps. Finally, all segmentation maps are combined to obtain a unique detailed segmentation mask. We test our model on both CT and MRI imaging data: the publicly available NIH Pancreas-CT dataset (consisting of 82 contrast-enhanced CTs) and a private MRI dataset (consisting of 40 MRI scans). Experimental results show that our model outperforms existing methods on CT pancreas segmentation, obtaining an average Dice score of about 88%, and yields promising segmentation performance on a very challenging MRI data set (average Dice score is about 77%). Additional control experiments demonstrate that the achieved performance is due to the combination of our 3D fully-convolutional deep network and the hierarchical representation decoding, thus substantiating our architectural design.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Hierarchical 3D Feature Learning for Pancreas Segmentation

Salanitri,

Bellitto,

Irmakci

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Mourad Gridach proposed PyDiNet [ 18 ] to improve the segmentation accuracy of image details by capturing small and complex changes in medical images while preserving global information with the dilation convolution of the pyramid structure. Yuan Wang et al [ 19 ] proposed a dual-input v-mesh fully convolutional network with the input of raw CT images and algorithmically processed images to improve the contrast between the pancreas and other soft tissues, and enhanced feature extraction by adding an attention module to improve the accuracy of pancreas segmentation. The above methods are able to handle the medical image segmentation problem in an improved way, being combined with FCN [ 20 ] or based on the U-Net network [ 21 ], and have achieved good segmentation results in respect of different medical images [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning

Zuo

et al. 2023

Sensors

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Accurate segmentation of the left atrial structure using magnetic resonance images provides an important basis for the diagnosis of atrial fibrillation (AF) and its treatment using robotic surgery. In this study, an image segmentation method based on sequence relationship learning and multi-scale feature fusion is proposed for 3D to 2D sequence conversion in cardiac magnetic resonance images and the varying scales of left atrial structures within different slices. Firstly, a convolutional neural network layer with an attention module was designed to extract and fuse contextual information at different scales in the image, to strengthen the target features using the correlation between features in different regions within the image, and to improve the network’s ability to distinguish the left atrial structure. Secondly, a recurrent neural network layer oriented to two-dimensional images was designed to capture the correlation of left atrial structures in adjacent slices by simulating the continuous relationship between sequential image slices. Finally, a combined loss function was constructed to reduce the effect of positive and negative sample imbalance and improve model stability. The Dice, IoU, and Hausdorff distance values reached 90.73%, 89.37%, and 4.803 mm, respectively, based on the LASC2013 (left atrial segmentation challenge in 2013) dataset; the corresponding values reached 92.05%, 89.41% and 9.056 mm, respectively, based on the ASC2018 (atrial segmentation challenge at 2018) dataset.

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Attention‐enhanced multiscale feature fusion network for pancreas and tumor segmentation

Dong,

Hu,

Zhu

et al. 2024

Medical Physics

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BackgroundAccurate pancreas and pancreatic tumor segmentation from abdominal scans is crucial for diagnosing and treating pancreatic diseases. Automated and reliable segmentation algorithms are highly desirable in both clinical practice and research.PurposeSegmenting the pancreas and tumors is challenging due to their low contrast, irregular morphologies, and variable anatomical locations. Additionally, the substantial difference in size between the pancreas and small tumors makes this task difficult. This paper proposes an attention‐enhanced multiscale feature fusion network (AMFF‐Net) to address these issues via 3D attention and multiscale context fusion methods.MethodsFirst, to prevent missed segmentation of tumors, we design the residual depthwise attention modules (RDAMs) to extract global features by expanding receptive fields of shallow layers in the encoder. Second, hybrid transformer modules (HTMs) are proposed to model deep semantic features and suppress irrelevant regions while highlighting critical anatomical characteristics. Additionally, the multiscale feature fusion module (MFFM) fuses adjacent top and bottom scale semantic features to address the size imbalance issue.ResultsThe proposed AMFF‐Net was evaluated on the public MSD dataset, achieving 82.12% DSC for pancreas and 57.00% for tumors. It also demonstrated effective segmentation performance on the NIH and private datasets, outperforming previous State‐Of‐The‐Art (SOTA) methods. Ablation studies verify the effectiveness of RDAMs, HTMs, and MFFM.ConclusionsWe propose an effective deep learning network for pancreas and tumor segmentation from abdominal CT scans. The proposed modules can better leverage global dependencies and semantic information and achieve significantly higher accuracy than the previous SOTA methods.

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Pancreas segmentation using a dual-input v-mesh network

Cited by 27 publications

References 24 publications

Hierarchical 3D Feature Learning for Pancreas Segmentation

Hierarchical 3D Feature Learning for Pancreas Segmentation

Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning

Attention‐enhanced multiscale feature fusion network for pancreas and tumor segmentation

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