Deep Dilated V-Net for 3D Volume Segmentation of Pancreas in CT images

Giddwani, Bharat; Tekchandani, Hitesh; Verma, Shrish

doi:10.1109/spin48934.2020.9071339

Cited by 15 publications

(19 citation statements)

References 15 publications

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“…In recent years,more and more deep learning algorithms have been proposed to segment abdominal organs in MR and CT images. Regard to pancreas segmentation, 2D deep network, 1,2 3D deep network, [3][4][5][6] Q learning, 7 surrounding organ-guided segmentation, 8 super pixel block, 9 atlas-based method 10 have been constantly…”

Section: Pancreas Segmentationmentioning

confidence: 99%

Semi‐supervised interactive fusion network for MR image segmentation

Bian

et al. 2023

Medical Physics

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Background: Medical image segmentation is an important task in the diagnosis and treatment of cancers. The low contrast and highly flexible anatomical structure make it challenging to accurately segment the organs or lesions. Purpose: To improve the segmentation accuracy of the organs or lesions in magnetic resonance (MR) images, which can be useful in clinical diagnosis and treatment of cancers. Methods: First,a selective feature interaction (SFI) module is designed to selectively extract the similar features of the sequence images based on the similarity interaction. Second, a multi-scale guided feature reconstruction (MGFR) module is designed to reconstruct low-level semantic features and focus on small targets and the edges of the pancreas. Third, to reduce manual annotation of large amounts of data, a semi-supervised training method is also proposed. Uncertainty estimation is used to further improve the segmentation accuracy. Results: Three hundred ninety-five 3D MR images from 395 patients with pancreatic cancer, 259 3D MR images from 259 patients with brain tumors, and four-fold cross-validation strategy are used to evaluate the proposed method. Compared to state-of -the-art deep learning segmentation networks, the proposed method can achieve better segmentation of pancreas or tumors in MR images. Conclusions: SFI-Net can fuse dual sequence MR images for abnormal pancreas or tumor segmentation. The proposed semi-supervised strategy can further improve the performance of SFI-Net.

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Section: Pancreas Segmentationmentioning

confidence: 99%

Semi‐supervised interactive fusion network for MR image segmentation

Bian

et al. 2023

Medical Physics

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“…A novel undisturbed mental state assessment prototype was proposed by Giddwani et al. ( 19 ). The recent pre-trained language models are also employed for disease early prediction ( 20 ) and clinical records classification ( 21 ).…”

Section: Related Workmentioning

confidence: 99%

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis

et al. 2022

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Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.

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“…Encouraged by the remarkable successes of deep learning in computer vision, deep convolutional neural networks (CNNs) have recently emerged as promising alternatives for medical image segmentation 7–24 . Compared with traditional methods using hand‐crafted features, deep learning methods can automatically learn hierarchical feature representations from the input image to attain end‐to‐end prediction.…”

Section: Introductionmentioning

confidence: 99%

“…However, the DDN is only integrated at the bottom layer of V-Net architecture. Hu et al 24 proposed a fully convolutional network with dilated convolution to segment the tumor on the breast ultrasound images. It used the dilated convolution layers to replace the max-pooling layers in stages 4 and 5 of the encoder.…”

Section: Introductionmentioning

confidence: 99%

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A novel end‐to‐end deep learning solution for coronary artery segmentation from CCTA

Dong

2022

Medical Physics

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Purpose Coronary computed tomographic angiography (CCTA) plays a vital role in the diagnosis of cardiovascular diseases, among which automatic coronary artery segmentation (CAS) serves as one of the most challenging tasks. To computationally assist the task, this paper proposes a novel end‐to‐end deep learning‐based (DL) solution for automatic CAS. Methods Inspired by the Di‐Vnet network, a fully automatic multistage DL solution is proposed. The new solution aims to preserve the integrity of blood vessels in terms of both their shape details and continuity. The solution is developed using 338 CCTA cases, among which 133 cases (33865 axial images) have their ground‐truth cardiac masks pre‐annotated and 205 cases (53365 axial images) have their ground‐truth coronary artery (CA) masks pre‐annotated. The solution's accuracy is measured using dice similarity coefficient (DSC), 95th percentile Hausdorff Distance (95% HD), Recall, and Precision scores for CAS. Results The proposed solution attains 90.29% in DSC, 2.11 mm in 95% HD, 97.02% in Recall, and 92.17% in Precision, respectively, which consumes 0.112 s per image and 30 s per case on average. Such performance of our method is superior to other state‐of‐the‐art segmentation methods. Conclusions The novel DL solution is able to automatically learn to perform CAS in an end‐to‐end fashion, attaining a high accuracy, efficiency and robustness simultaneously.

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Deep Dilated V-Net for 3D Volume Segmentation of Pancreas in CT images

Cited by 15 publications

References 15 publications

Semi‐supervised interactive fusion network for MR image segmentation

Semi‐supervised interactive fusion network for MR image segmentation

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis

A novel end‐to‐end deep learning solution for coronary artery segmentation from CCTA

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