Highlights • The multi-channel fully convolutional networks is designed. • We segment liver tumors from multiphase contrast-enhanced CT images. • We train one network for each phase of CT images and ...fuse their high-layer features together. • This method can make full use of the characteristics of different enhancement phases of CT images. • The results showed our model provided greater accuracy and robustness than previous methods.
We propose a novel method for object reconstruction of ghost imaging based on Pseudo-Inverse, where the original objects are reconstructed by computing the pseudo-inverse of the matrix constituted by ...the row vectors of each speckle field. We conduct reconstructions for binary images and gray-scale images. With equal number of measurements, our method presents a satisfying performance on enhancing Peak Signal to Noise Ratio (PSNR) and reducing computing time. Being compared with the other existing methods, its PSNR distinctly exceeds that of the traditional Ghost Imaging (GI) and Differential Ghost Imaging (DGI). In comparison with the Compressive-sensing Ghost Imaging (CGI), the computing time is substantially shortened, and in regard to PSNR our method exceeds CGI on grayscale images and performs as well as CGI visually on binary images. The influence of both the detection noise and the accuracy of measurement matrix on PSNR are also presented.
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.
Terahertz lensless phase retrieval imaging is a promising technique for non-destructive inspection applications. In the conventional multiple-plane phase retrieval method, the convergence speed due ...to wave propagations and measures with equal interval distance is slow and leads to stagnation. To address this drawback, we propose a nonlinear unequal spaced measurement scheme in which the interval space between adjacent measurement planes is gradually increasing, it can significantly increase the diversity of the intensity with a smaller number of required images. Both the simulation and experimental results demonstrate that our method enables quantitative phase and amplitude imaging with a faster speed and better image quality, while also being computationally efficient and robust to noise.
Under the framework of compressed sensing theory, the greedy algorithm achieves good reconstruction performance with known signal sparsity. However, unknown sparsity of sparse signals in practical ...applications brings obstacles for signal reconstruction. Specifically, the conventional sparsity adaptive adjustment algorithm takes long time to finish the reconstruction, and the accuracy of reconstruction is not good enough. To solve this problem, this paper proposes a new matching pursuit reconstruction algorithm based on bidirectional sparsity adaptive adjustment and weak selection of atoms (BSA-WSAMP). In this algorithm, the optimization strategy for atom weak selection is employed to update the support set, and the idea of "zoom" bidirectional variable step-size is applied to achieve the sparsity adaptive adjustment. Based on this, the number of iterations can be reduced effectively, and the accurate reconstruction of the sparse signal is obtained. Simulation results indicate that the proposed BSA-WSAMP algorithm achieves better adaptive characteristic of the sparsity, higher reconstruction quality, lower reconstruction complexity, and less reconstruction time than some existing reconstruction algorithms.
Abstract This paper presents a new level set method for segmentation of cardiac left and right ventricles. We extend the edge based distance regularized level set evolution (DRLSE) model in to a ...two-level-set formulation, with the 0-level set and k -level set representing the endocardium and epicardium, respectively. The extraction of endocardium and epicardium is obtained as a result of the interactive curve evolution of the 0 and k level sets derived from the proposed variational level set formulation. The initialization of the level set function in the proposed two-level-set DRLSE model is generated from roughly located endocardium, which can be performed by applying the original DRLSE model. Experimental results have demonstrated the effectiveness of the proposed two-level-set DRLSE model.
Significant inherent extra-articular varus angulation is associated with abnormal postoperative hip-knee-ankle (HKA) angle. At present, HKA is manually measured by orthopedic surgeons and it ...increases the doctors' workload. To automatically determine HKA, a deep learning-based automated method for measuring HKA on the unilateral lower limb X-rays was developed and validated. This study retrospectively selected 398 double lower limbs X-rays during 2018 and 2020 from Jilin University Second Hospital. The images (n = 398) were cropped into unilateral lower limb images (n = 796). The deep neural network was used to segment the head of hip, the knee, and the ankle in the same image, respectively. Then, the mean square error of distance between each internal point of each organ and the organ's boundary was calculated. The point with the minimum mean square error was set as the central point of the organ. HKA was determined using the coordinates of three organs' central points according to the law of cosines. In a quantitative analysis, HKA was measured manually by three orthopedic surgeons with a high consistency (176.90 ° ± 12.18°, 176.95 ° ± 12.23°, 176.87 ° ± 12.25°) as evidenced by the Kandall's W of 0.999 (p < 0.001). Of note, the average measured HKA by them (176.90 ° ± 12.22°) served as the ground truth. The automatically measured HKA by the proposed method (176.41 ° ± 12.08°) was close to the ground truth, showing no significant difference. In addition, intraclass correlation coefficient (ICC) between them is 0.999 (p < 0.001). The average of difference between prediction and ground truth is 0.49°. The proposed method indicates a high feasibility and reliability in clinical practice.
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.
...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.
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.
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.
We presented a novel ghost imaging scheme based on fuzzy c-means clustering (FCM) to reduce measurements and improve the visibility of the reconstruction image. Different from the GI methods, the FCM ...model is first employed to partition the intensity values of the reference light path and probe light path. Then, the relative speckle patterns and bucket intensity values are selected with respect to the clustering results. Finally, the object can be obtained by conventional GI methods. From the considerable simulations and experimental results, we conclude that the proposed scheme can enhance the visibility of the reconstruction image by using much fewer data from measurements compared with the existing GI methods.
Owing to the irregular shape and high anatomical variability of the pancreas in abdominal CT images, pancreas segmentation is regarded as a challenging task. To address this issue, we propose an ...automatic segmentation model using double adversarial networks with a pyramidal pooling module. First, we introduce double adversarial networks that double-check whether the obtained segmentation results are similar to their ground truths owing to the special competing mechanism of adversarial learning, which contributes to the capturing of spatial information for segmentation and prompts the obtained samples to be more realistic, to improve the network segmentation performance. Second, we design a pyramidal pooling module to collect multi-level features and retain substantial information for segmentation in order to further boost the network performance. Finally, to assess the segmentation performance of our model, we use several indexes, namely the Dice similarity coefficient (DSC), Jaccard index, precision, and recall, as evaluation indicators. Experimental results show that the proposed model outperforms most existing pancreas segmentation methods.
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