Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Sun, Muyi; Zhang, Guanhong; Dang, Hao; Qi, Xingqun; Zhou, Xiaoguang; Chang, Qing

doi:10.1109/access.2019.2918800

Cited by 54 publications

(39 citation statements)

References 36 publications

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“…To our best knowledge, this is the first study to use the deep learning method to predict the nuclear BAP1 expression status in ophthalmic histopathology images. Furthermore, previous studies that have combined medical image analysis with deep learning have mainly focused on the pathological diagnosis and partially in computer engineering applications such as gastric cancer segmentation in digital pathology images [50], polyp detection in endoscope images [27], and lung cancer detection in computed tomography (CT) scans [51]. In our work, we concentrated more on the combination of bioinformatics research with deep learning based computer vision methods, which is more similar to simple medical research, and not computer engineering.…”

Section: Discussionmentioning

confidence: 99%

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks

Sun

Zhou

et al. 2019

Cancers

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Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.

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Section: Discussionmentioning

confidence: 99%

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks

Sun

Zhou

et al. 2019

Cancers

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“…3, the deformable convolution shows better adaption for liver in a CT image than the vanilla convolution. In fact, Sun et al [62] have started to explore the utilization of deformable convolution on automatic segmentation networks for gastric cancer, and their proposed network achieves better segmentation results than vanilla U-Net [28] and ResU-Net [61].…”

Section: A Deformable Encodingmentioning

confidence: 99%

DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation

Lei¹,

Wang

Zhang

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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Deep convolutional neural networks have been widely used for medical image segmentation due to their superiority in feature learning. Although these networks are successful for simple object segmentation tasks, they suffer from two problems for liver and liver tumor segmentation in CT images. One is that convolutional kernels of fixed geometrical structure are unmatched with livers and liver tumors of irregular shapes. The other is that pooling and strided convolutional operations easily lead to the loss of spatial contextual information of images. To address these issues, we propose a deformable encoder-decoder network (DefED-Net) for liver and liver tumor segmentation. The proposed network makes two contributions. The first is that the deformable convolution is used to enhance the feature representation capability of DefED-Net, which can help the network to learn convolution kernels with adaptive spatial structuring information. The second is that we design a Ladder-atrous-spatial-pyramid-pooling module using multi-scale dilation rate (Ladder-ASPP) and apply the module to learn better context information than the atrous spatial pyramid pooling (ASPP) for CT image segmentation. The proposed DefED-Net is evaluated on two public benchmark datasets, the LiTS and the 3DIRCADb. Experiments demonstrate that the DefED-Net has better capability of feature representation as well as provides higher accuracy on liver and liver tumor segmentation than stateof-the art networks. The available code of DefED-Net we propose can be found from https://github.com/SUST-reynole/DefED-Net.

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“…Qu et al[ 29 ] presented a novel type of intermediate dataset and developed a stepwise fine-tuning-based scheme to improve the classification performance of deep neural networks. Sun et al[ 30 ] also demonstrated that the proposed DL model was a powerful image segmentation tool with 91.60% for the mean accuracy and 82.65% for the mean IoU. Another study also demonstrated that the Mask R-CNN model was an effective method to target the field of medical image segmentation[ 31 ].…”

Section: Ai In the Diagnosis Of Gastric Cancermentioning

confidence: 99%

Artificial intelligence in gastric cancer: Application and future perspectives

Niu¹,

Zhao²,

Wu³

et al. 2020

WJG

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Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

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Accurate Gastric Cancer Segmentation in Digital Pathology Images Using Deformable Convolution and Multi-Scale Embedding Networks

Cited by 54 publications

References 36 publications

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks

Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks

DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation

Artificial intelligence in gastric cancer: Application and future perspectives

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