Pancreas and cyst segmentation

Dmitriev, Konstantin; Gutenko, Ievgeniia; Nadeem, Saad; Kaufman, Arie

doi:10.1117/12.2216537

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…There are two types of k, that is, one is the smoothness function k (1) , and another is the appearance function k (2) . k (1) and k (2) are defined in (10) and 11, respectively k (1)…”

Section: Post-processing Of Pancreas Segmentation By 3-d Fully Conmentioning

confidence: 99%

“…Patients are frequently examined by the early parenchyma phase abdominal CT [7]. In upper abdominal surgery, such as laparoscopic gastrectomy or pancreatectomy, the location of the pancreas is required for enabling safer surgical procedure [10] whereas, manual delineation for the pancreas is time consuming and often irreproducible. Therefore, there is a calling need for developing an efficient computer-aided segmentation method to help physicians diagnose and assess the progression of diabetes or pancreatic cancer, as done in other applications [11]- [14].…”

Section: Introductionmentioning

confidence: 99%

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Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Xue

Nie

et al. 2021

IEEE Trans. Cybern.

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Automatic pancreas segmentation is crucial to the diagnostic assessment of diabetes or pancreatic cancer. However, the relatively small size of the pancreas in the upper body, as well as large variations of its location and shape in retroperitoneum, make the segmentation task challenging. To alleviate these challenges, in this article, we propose a cascaded multitask 3-D fully convolution network (FCN) to automatically segment the pancreas. Our cascaded network is composed of two parts. The first part focuses on fast locating the region of the pancreas, and the second part uses a multitask FCN with dense connections to refine the segmentation map for fine voxel-wise segmentation. In particular, our multitask FCN with dense connections is implemented to simultaneously complete tasks of the voxel-wise segmentation and skeleton extraction from the pancreas. These two tasks are complementary, that is, the extracted skeleton provides rich information about the shape and size of the pancreas in retroperitoneum, which can boost the segmentation of pancreas. The multitask FCN is also designed to share Manuscript

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“…There are two types of k, that is, one is the smoothness function k (1) , and another is the appearance function k (2) . k (1) and k (2) are defined in (10) and 11, respectively k (1)…”

Section: Post-processing Of Pancreas Segmentation By 3-d Fully Conmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Xue

Nie

et al. 2021

IEEE Trans. Cybern.

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“…The effectiveness and the robustness of the ensuing classification algorithm depend on the precision of the segmentation outlines. The outlines of each cyst (if multiple) within the pancreas were obtained by a semi-automated graph-based segmentation technique [3] (Fig. 1), and were confirmed by an experienced radiologist (E.F.).…”

Section: Data Acquisitionmentioning

confidence: 99%

Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble

Dmitriev

Kaufman

Javed

et al. 2017

Lecture Notes in Computer Science

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There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information. Quantitative assessment of the proposed method was performed using a 10-fold cross validation on 134 patients and reported a classification accuracy of 83.6%.

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“…Organ segmentation from medical imaging is recognized as a difficult job, since the contours of organs tend to be indistinguishable from the background gray low-resolution images. Especially for organs with small volume and varied morphology, such as pancreas [ 1 ]. As deep learning thrives, convolutional neural networks (CNNs) show great potential on organ segmentation tasks and various methods based on CNNs have been raised for pancreas segmentation [ 2 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism

Lian

Wang

et al. 2021

BMC Med Imaging

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Background A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset. Methods The 82 pancreatic contrast-enhanced abdominal CT volumes were split via four-fold cross validation to test the model performance. In order to achieve accurate segmentation, we firstly involved residual learning into an adversarial U-Net to achieve a better gradient information flow for improving segmentation performance. Then, we introduced a multi-level pyramidal pooling module (MLPP), where a novel pyramidal pooling was involved to gather contextual information for segmentation, then four groups of structures consisted of a different number of pyramidal pooling blocks were proposed to search for the structure with the optimal performance, and two types of pooling blocks were applied in the experimental section to further assess the robustness of MLPP for pancreas segmentation. For evaluation, Dice similarity coefficient (DSC) and recall were used as the metrics in this work. Results The proposed method preceded the baseline network 5.30% and 6.16% on metrics DSC and recall, and achieved competitive results compared with the-state-of-art methods. Conclusions Our algorithm showed great segmentation performance even on the particularly challenging pancreas dataset, this indicates that the proposed model is a satisfactory and promising segmentor.

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Pancreas and cyst segmentation

Cited by 15 publications

References 6 publications

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble

Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism

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