Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

Guo, Yi; Jiang, Fei; Xu, Leiming; Shen, Feng; Jin, Zhendong; Wang, Yuanyuan

doi:10.1109/access.2019.2963472

Cited by 18 publications

(11 citation statements)

References 16 publications

(25 reference statements)

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“…Nerve segmentation [151], [222] Base U-net [50] Inception block [223] Residual block [224] Modified parallel U-net Breast lesion [225] Base U-net [39] Attention gate Arterial wall [226] Base U-net Cardiovascular structures [227] Base U-net Fetal head [219] Base U-net Gastrointestinal tumor [228] Base U-net Knee cartilage [96] Modified U-net with dual parallel encoders Preterm birth prediction [220] Base U-net Thyroid [229] Residual block Transcranial detection [221] Base U-net Ovary detection [230] Base U-net…”

Section: Reference Model/methods Usedmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Reference Model/methods Usedmentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Another study by Hu et al evaluated the automatic segmentation of breast tumors on still images of breast ultrasounds and showed a mean Dice similarity coefficient of 88.97% using a dilated fully convolutional network (DFCN) [31]. Li et al evaluated the automatic segmentation of gastrointestinal stromal tumors in EUS images using a multi-task refined boundary-supervision U-net (MRBSU-Net) and showed a mean Dice similarity coefficient of 92% [32]. Although the used CNN and the subject were different between the studies, the concordance rates of these three studies were around 90% and were considered as favorable in comparison with that of our study (the median IoU of 0.77).…”

Section: Discussionmentioning

confidence: 99%

“…In automatic tumor segmentation in breast ultrasound images, a study by Hu et al compared six different algorithms-VGG-16 network, U-Net, dilated residual network, DFCN with and without dilated convolution, DFCN with a phase-based active contour model-that were fine-tuned from a pre-trained model and showed DFCN with a phase-based active contour model showed the highest Dice similarity coefficient [31]. As for EUS images, Li et al compared five different algorithms-U-Net, RU-Net, multi-task RU-Net, refined boundary-supervision U-net (RBSU-Net), and MRBSU-Net-in the automatic segmentation of gastrointestinal stromal tumors, and MRBSU-Net showed the highest concordance [32]. However, which algorithm is better in automatic segmentation for a certain subject of EUS video image has been rarely reported and is a question for future research.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation of Pancreatic Tumors Using Deep Learning on a Video Image of Contrast-Enhanced Endoscopic Ultrasound

Iwasa

Iwashita

Takeuchi

et al. 2021

JCM

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Background: Contrast-enhanced endoscopic ultrasound (CE-EUS) is useful for the differentiation of pancreatic tumors. Using deep learning for the segmentation and classification of pancreatic tumors might further improve the diagnostic capability of CE-EUS. Aims: The aim of this study was to evaluate the capability of deep learning for the automatic segmentation of pancreatic tumors on CE-EUS video images and possible factors affecting the automatic segmentation. Methods: This retrospective study included 100 patients who underwent CE-EUS for pancreatic tumors. The CE-EUS video images were converted from the originals to 90-second segments with six frames per second. Manual segmentation of pancreatic tumors from B-mode images was performed as ground truth. Automatic segmentation was performed using U-Net with 100 epochs and was evaluated with 4-fold cross-validation. The degree of respiratory movement (RM) and tumor boundary (TB) were divided into 3-degree intervals in each patient and evaluated as possible factors affecting the segmentation. The concordance rate was calculated using the intersection over union (IoU). Results: The median IoU of all cases was 0.77. The median IoUs in TB-1 (clear around), TB-2, and TB-3 (unclear more than half) were 0.80, 0.76, and 0.69, respectively. The IoU for TB-1 was significantly higher than that of TB-3 (p < 0.01). However, there was no significant difference between the degrees of RM. Conclusion: Automatic segmentation of pancreatic tumors using U-Net on CE-EUS video images showed a decent concordance rate. The concordance rate was lowered by an unclear TB but was not affected by RM.

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“…A group of researchers ( Kumar et al, 2018 ) proposed U-SegNet that is a hybrid of SegNet and U-Net segmentation architectures to improve automated brain tissue segmentation. A research work ( Li et al, 2020 ) proposed multi-task refined boundary-supervision U-Net (MRBSU-Net) for gastrointestinal stromal tumor (GIST) segmentation from endoscopic ultrasound (EUS) images. Also, a group of researchers ( Lou et al, 2020 ) employed a U-Net architecture with semiautomatic labeling to segment the esophagus from CT images.…”

Section: Related Workmentioning

confidence: 99%

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

Abedalla

Abdullah

Al‐Ayyoub

et al. 2021

PeerJ Computer Science

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Medical imaging refers to visualization techniques to provide valuable information about the internal structures of the human body for clinical applications, diagnosis, treatment, and scientific research. Segmentation is one of the primary methods for analyzing and processing medical images, which helps doctors diagnose accurately by providing detailed information on the body’s required part. However, segmenting medical images faces several challenges, such as requiring trained medical experts and being time-consuming and error-prone. Thus, it appears necessary for an automatic medical image segmentation system. Deep learning algorithms have recently shown outstanding performance for segmentation tasks, especially semantic segmentation networks that provide pixel-level image understanding. By introducing the first fully convolutional network (FCN) for semantic image segmentation, several segmentation networks have been proposed on its basis. One of the state-of-the-art convolutional networks in the medical image field is U-Net. This paper presents a novel end-to-end semantic segmentation model, named Ens4B-UNet, for medical images that ensembles four U-Net architectures with pre-trained backbone networks. Ens4B-UNet utilizes U-Net’s success with several significant improvements by adapting powerful and robust convolutional neural networks (CNNs) as backbones for U-Nets encoders and using the nearest-neighbor up-sampling in the decoders. Ens4B-UNet is designed based on the weighted average ensemble of four encoder-decoder segmentation models. The backbone networks of all ensembled models are pre-trained on the ImageNet dataset to exploit the benefit of transfer learning. For improving our models, we apply several techniques for training and predicting, including stochastic weight averaging (SWA), data augmentation, test-time augmentation (TTA), and different types of optimal thresholds. We evaluate and test our models on the 2019 Pneumothorax Challenge dataset, which contains 12,047 training images with 12,954 masks and 3,205 test images. Our proposed segmentation network achieves a 0.8608 mean Dice similarity coefficient (DSC) on the test set, which is among the top one-percent systems in the Kaggle competition.

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Multi-Task Refined Boundary-Supervision U-Net (MRBSU-Net) for Gastrointestinal Stromal Tumor Segmentation in Endoscopic Ultrasound (EUS) Images

Cited by 18 publications

References 16 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Automatic Segmentation of Pancreatic Tumors Using Deep Learning on a Video Image of Contrast-Enhanced Endoscopic Ultrasound

Chest X-ray pneumothorax segmentation using U-Net with EfficientNet and ResNet architectures

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