Attention-guided duplex adversarial U-net for pancreatic segmentation from computed tomography images.

PURPOSE: Segmenting the organs from computed tomography (CT) images is crucial to early diagnosis and treatment. Pancreas segmentation is especially challenging because the pancreas has a small volume and a large variation in shape. METHODS: To mitigate this issue, an attention-guided duplex adversarial U-Net (ADAU-Net) for pancreas segmentation is proposed in this work. First, two adversarial networks are integrated into the baseline U-Net to ensure the obtained prediction maps resemble the ground truths. Then, attention blocks are applied to preserve much contextual information for segmentation. The implementation of the proposed ADAU-Net consists of two steps: 1) backbone segmentor selection scheme is introduced to select an optimal backbone segmentor from three two-dimensional segmentation model variants based on a conventional U-Net and 2) attention blocks are integrated into the backbone segmentor at several locations to enhance the interdependency among pixels for a better segmentation performance, and the optimal structure is selected as a final version. RESULTS: The experimental results on the National Institutes of Health Pancreas-CT dataset show that our proposed ADAU-Net outperforms the baseline segmentation network by 6.39% in dice similarity coefficient and obtains a competitive performance compared with the-state-of-art methods for pancreas segmentation. CONCLUSION: The ADAU-Net achieves satisfactory segmentation results on the public pancreas dataset, indicating that the proposed model can segment pancreas outlines from CT images accurately.

Multi-scale Selection and Multi-channel Fusion Model for Pancreas Segmentation Using Adversarial Deep Convolutional Nets.

Organ segmentation from existing imaging is vital to the medical image analysis and disease diagnosis. However, the boundary shapes and area sizes of the target region tend to be diverse and flexible. And the frequent applications of pooling operations in traditional segmentor result in the loss of spatial information which is advantageous to segmentation. All these issues pose challenges and difficulties for accurate organ segmentation from medical imaging, particularly for organs with small volumes and variable shapes such as the pancreas. To offset aforesaid information loss, we propose a deep convolutional neural network (DCNN) named multi-scale selection and multi-channel fusion segmentation model (MSC-DUnet) for pancreas segmentation. This proposed model contains three stages to collect detailed cues for accurate segmentation: (1) increasing the consistency between the distributions of the output probability maps from the segmentor and the original samples by involving the adversarial mechanism that can capture spatial distributions, (2) gathering global spatial features from several receptive fields via multi-scale field selection (MSFS), and (3) integrating multi-level features located in varying network positions through the multi-channel fusion module (MCFM). Experimental results on the NIH Pancreas-CT dataset show that our proposed MSC-DUnet obtains superior performance to the baseline network by achieving an improvement of 5.1% in index dice similarity coefficient (DSC), which adequately indicates that MSC-DUnet has great potential for pancreas segmentation.

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

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.

Dual adversarial convolutional networks with multilevel cues for pancreatic segmentation.

Accurate organ segmentation is a relatively challenging subject in medical imaging, especially for the pancreas, whose morphological characteristics are subtle but variable. In this paper, a novel dual adversarial convolutional network with multilevel cues (DACN-MC) is proposed to segment the pancreas in computerized tomography (CT). DACN-MC first involves a duplex adversarial network using a conventional model for biomedical image segmentation, which ensures the veracity of the predicted probability volumes and ultimately enhances the quality of the obtained maps. Specifically, one of the adversarial networks helps the predicted maps to resemble the ground truths by importing extra guidance into the original loss functions. The other adversarial network further judges whether the obtained maps are well segmented and improves the image quality once again. Then, a multilevel cue collection module (MCCM) is introduced to gather many useful details for pancreas segmentation. In other words, we collect several sets of material formed by features from different layers and pick out a group with optimal performance for use in the ultimate algorithm. The experimental results show that dual adversarial convolutional networks together with multilevel cue collection help our proposed algorithm to achieve competitive segmentation performance, based on the results of several evaluation indexes.

Pancreas segmentation based on an adversarial model under two-tier constraints.

Pancreas segmentation is vital for the effective diagnosis and treatment of diabetic or pancreatic diseases. However, the irregular shape and strong variability of the pancreas in medical images pose significant challenges to accurate segmentation. In this paper, we propose a novel segmentation algorithm that imposes two-tier constraints on a conventional network through adversarial learning, namely UDCGAN. Specifically, we incorporate a dual adversarial training scheme in a conventional segmentation network, which further facilitates the probability maps from the segmentor to converge on the ground truth distributions owing to the effectiveness of generative adversarial networks (GANs) in capturing data distributions. This novel segmentation algorithm is equivalent to employing adversarial learning on a segmentation network that has been trained in an adversarial manner. Duplex intervention and guidance further refine the loss functions of the segmentor, thus effectively contributing to the preservation of details for segmentation. The segmentation results on the NIH Pancreas-CT dataset show that our proposed model achieves a competitive performance compared with other state-of-the-art methods.