Automated Pancreas Segmentation Research Articles

DRAN: Deep recurrent adversarial network for automated pancreassegmentation

Automated pancreas segmentation in abdominal computed tomography (CT) scans is of high clinical relevance (i.e. pancreas cancer diagnosis and prognosis), but extremely difficult because the pancreas is a soft, small, and flexible abdominal organ with high anatomical variability, which causes the previous segmentation methods to result in low precision. In this study, the authors present a new deep recurrent adversarial network (DRAN) to tackle this challenge. DRAN contains three steps: (i) preserving global resolution of CT scans and modifying the receptive field of kernel adaptively through a dilated convolution autoencoder module; (ii) modelling contextual spatial correlation between neighbouring CT scan patches benefits from a specially designed local long short-term memory module; and (iii) improving the performance and generalisation by leveraging an adversarial module, which can constrain the spatial smoothness consistency between continuous CT scans based on the long-range spatial interaction. The system is evaluated on a dataset of 80 manually segmented CT volumes, using four-fold cross-validation. Its performance surpasses other state-of-the-art methods, with the Dice similarity coefficient of 89.87 ± 3.17 % and pixel-wise accuracy of 95.85 ± 3.04 % . Also, they perform a qualitative evaluation by an expert further revealing the effectiveness and potential of their DRAN as a clinical segmentation tool.

Top-Down Method Used for Pancreas Segmentation

Image segmentation is actively an imperative title role in image analysis. Image segmentation is advantageous in many applications like traffic detection, surface crack identification, medical image analysis, face recognition, crop disease detection. Two Approaches are used for automatic pancreas segmentation. Top-Down and Bottom-Up approach used for CT image segmentation. In Top Down approach, Grey Level Co-occurrence Matrix, Simple Linear Iterative Clustering, Scale-Invariant Feature transform, Novel Modified Kernel fuzzy c-means clustering (NMKFCM) and Kernel Density Estimator methods used and automatic bottomup technique is used for pancreas subgrouping in C.T. scans. Top-Dow approach accuracy rate is less than bottom-up approach. Top-down approach required less time period as compare to bottom-up approach. In top-down approach, input image manually selected and processed it. KDE, NMKFCM and SIFT are used to detect feature of image. NMKFCM works on neighborhood point value. In KDE, Edge detection based on the kernel estimation of the probability density function .In SIFT, comprehensive information of local feature of image is focused.

Cascaded MultiTask 3-D Fully Convolutional Networks for Pancreas Segmentation

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 the low- and mid-level features across the tasks. A feature consistency module is further introduced to enhance the connection and fusion of different levels of feature maps. Evaluations on two pancreas datasets demonstrate the robustness of our proposed method in correctly segmenting the pancreas in various settings. Our experimental results outperform both baseline and state-of-the-art methods. Moreover, the ablation study shows that our proposed parts/modules are critical for effective multitask learning.

Pancreas Segmentation in Abdominal CT Scans using Inter-/Intra-Slice Contextual Information with a Cascade Neural Network.

Automatic pancreas segmentation with high precision in Computed Tomography (CT) images is a fundamental issue in both medical image analysis and computer-aided diagnosis (CAD). However, pancreas segmentation is challenging because of the high variability in location and anatomy of the organs, while occupying only a very small part of the entire abdominal CT scans. Due to the rapid development of the CAD system and the urgent need for clinical treatment, the pancreas image segmentation with high precision is demanded. In this paper, we propose a new approach for automatic pancreas segmentation of CT images using inter-/intra-slice contextual information with a cascade neural network. Fully convolutional neural networks (FCN) are used to extract intra-slice contextual information for pancreas segmentation. Recurrent neural networks (RNNs) is introduced to extract inter-slice contextual information. With the setting bounding boxes, the proposed method outperforms the state-of-the-arts with an average Dice Similarity Coefficient (DSC) of 87.72 for NIH dataset with 4-fold cross-validation.

Morphological and multi-level geometrical descriptor analysis in CT and MRI volumes for automatic pancreas segmentation

Automatic pancreas segmentation in 3D radiological scans is a critical, yet challenging task. As a prerequisite for computer-aided diagnosis (CADx) systems, accurate pancreas segmentation could generate both quantitative and qualitative information towards establishing the severity of a condition, and thus provide additional guidance for therapy planning. Since the pancreas is an organ of high inter-patient anatomical variability, previous segmentation approaches report lower quantitative accuracy scores in comparison to abdominal organs such as the liver or kidneys. This paper presents a novel approach for automatic pancreas segmentation in magnetic resonance imaging (MRI) and computer tomography (CT) scans. This method exploits 3D segmentation that, when coupled with geometrical and morphological characteristics of abdominal tissue, classifies distinct contours in tight pixel-range proximity as “pancreas” or “non-pancreas”. There are three main stages to this approach: (1) identify a major pancreas region and apply contrast enhancement to differentiate between pancreatic and surrounding tissue; (2) perform 3D segmentation via continuous max-flow and min-cuts approach, structured forest edge detection, and a training dataset of annotated pancreata; (3) eliminate non-pancreatic contours from resultant segmentation via morphological operations on area, structure and connectivity between distinct contours. The proposed method is evaluated on a dataset containing 82 CT image volumes, achieving mean Dice Similarity coefficient (DSC) of 79.3 ± 4.4%. Two MRI datasets containing 216 and 132 image volumes are evaluated, achieving mean DSC 79.6 ± 5.7% and 81.6 ± 5.1% respectively. This approach is statistically stable, reflected by lower metrics in standard deviation in comparison to state-of-the-art approaches.

Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks.

Automated pancreas segmentation in medical images is a prerequisite for many clinical applications, such as diabetes inspection, pancreatic cancer diagnosis, and surgical planing. In this paper, we formulate pancreas segmentation in magnetic resonance imaging (MRI) scans as a graph based decision fusion process combined with deep convolutional neural networks (CNN). Our approach conducts pancreatic detection and boundary segmentation with two types of CNN models respectively: 1) the tissue detection step to differentiate pancreas and non-pancreas tissue with spatial intensity context; 2) the boundary detection step to allocate the semantic boundaries of pancreas. Both detection results of the two networks are fused together as the initialization of a conditional random field (CRF) framework to obtain the final segmentation output. Our approach achieves the mean dice similarity coefficient (DSC) 76.1% with the standard deviation of 8.7% in a dataset containing 78 abdominal MRI scans. The proposed algorithm achieves the best results compared with other state of the arts.

EP-1643: Automated pancreas segmentation and motion tracking based on dynamic MRI

EP-1643: Automated pancreas segmentation and motion tracking based on dynamic MRI

Model-Based Pancreas Segmentation in Portal Venous Phase Contrast-Enhanced CT Images

This study aims to automatically detect and segment the pancreas in portal venous phase contrast-enhanced computed tomography (CT) images. The institutional review board of the University of Erlangen-Nuremberg approved this study and waived the need for informed consent. Discriminative learning is used to build a pancreas tissue classifier incorporating spatial relationships between the pancreas and surrounding organs and vessels. Furthermore, discrete cosine and wavelet transforms are used to build texture features to describe local tissue appearance. Classification is used to guide a constrained statistical shape model to fit the data. The algorithm to detect and segment the pancreas was evaluated on 40 consecutive CT data that were acquired in the portal venous contrast agent phase. Manual segmentation of the pancreas was carried out by experienced radiologists and served as reference standard. Threefold cross validation was performed. The algorithm-based detection and segmentation yielded an average surface distance of 1.7 mm and an average overlap of 61.2 % compared with the reference standard. The overall runtime of the system was 20.4 min. The presented novel approach enables automatic pancreas segmentation in portal venous phase contrast-enhanced CT images which are included in almost every clinical routine abdominal CT examination. Reliable pancreatic segmentation is crucial for computer-aided detection systems and an organ-specific decision support.

Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography

We propose an automated pancreas segmentation algorithm from contrast-enhanced multiphase computed tomography (CT) and verify its effectiveness in segmentation. The algorithm is characterized by three unique ideas. First, a two-stage segmentation strategy with spatial standardization of pancreas was employed to reduce variations in the pancreas shape and location. Second, patient- specific probabilistic atlas guided segmentation was developed to cope with the remaining variability in shape and location. Finally, a classifier ensemble was incorporated to refine the rough segmentation results. The effectiveness of the proposed algorithm was validated with 20 unknown CT volumes, as well as three on-site CT volumes distributed in a competition of pancreas segmentation algorithms. The experimental results indicated that the segmentation performance was enhanced by the proposed algorithm, and the Jaccard index between an extracted pancreas and a true one was 57.9%. This study verified the effectiveness of two-stage segmentation with spatial standardization of pancreas in delineating the pancreas region, patient-specific probabilistic atlas guided segmentation in reducing false negatives, and a classifier ensemble in boosting segmentation performance.