Subcortical brain segmentation is a challenging task due to the anatomical variability in both shape and size between patients, such as thalamus, hippocampus and amygdala. It requires the accurate segmentation of these structures to measure their volume and surface. However, few methods can obtain accurate segmentation because the boundaries of these structures are obscure in MR images. We propose an attention-based convolutional neural network for subcortical brain segmentation. In our method, image clipping is firstly applied for pre-processing. Accurate subcortical brain segmentation is obtained by using attention-based convolutional neural network. Maximum connectivity is finally applied for post-processing. Experimental results in 35 subjects showed that the proposed method segment the brain region with higher accuracy than other methods. The Dice, TPR and VD measures show that the proposed method is able to provide a precise and robust segmentation estimate. The proposed method is a suitable alternative to assist the manual subcortical brain segmentation task.
Heart segmentation is challenging due to the poor image contrast of heart in the CT images. Since manual segmentation of the heart is tedious and time-consuming, we propose an attention-based Convolution Neural Network (CNN) for heart segmentation. First, one-hot preprocessing is performed on the multi-tissue CT images. U-Net network with Attention-gate is then applied to obtain the heart region. We compared our method with several CNN methods in terms of dice coefficient. Results show that our method outperforms other methods for segmentation.
We propose a novel method for false positive reduction of pulmonary nodules using three-channel samples with different average thickness. A three-channel sample contains a patch centered on the candidate point as well as two patches at the k-th slice above and below the candidate point. Three-channel samples include rich spatial contextual information of pulmonary nodules, and can be trained with a low computational and storage requirement. The convolutional neural networks (CNNs) are constructed and optimized as the feature extractor and classifier of candidates in our study. A fusion method is proposed for fusing multiple prediction results of each candidate. Our method reports high sensitivities of 84.8% and 91.4% at 4 and 8 false positives per scan respectively on 888 CT scans released by the LUNA16 Challenge. The experimental results show that our method significantly reduces false positives in pulmonary nodule detection.
In this paper, we proposed a semi-automatic pulmonary nodule segmentation algorithm, which is operated within a region of interest for each nodule. It mainly includes two parts: the unsupervised training of auto-encoder and the supervised training of segmentation network. Applying an auto-encoder's unsupervised learning, we obtain a feature extractor that consists of its encoded part. Through adding some new neural network layers behind the feature extractor and do supervised learning on it, we get the final segmentation neural network. Compared with the traditional maximum two-dimensional entropy threshold segmentation algorithm, the dice correlation coefficient of this algorithm is 1% - 9% higher in 36 regions of interest segmentation experiments.
This work achieves a method based on modified extreme learning machine (ELM) with deep convolutional features to detect lung nodules automatically. Convolutional neural networks (CNNs) are employed to extract the features of lung nodules for classification. And then ELM is used to detect the lung nodules by combining the normalization and vote selection. In comparison with the traditional methods, it is shown that our method achieves a higher performance and it can be used as an effective tool for lung nodules computer aided diagnosis.
Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.
Our purposes are to develop a vertebra detection scheme for automated scan planning, which would assist radiological technologists in their routine work for the imaging of vertebrae. Because the orientations of vertebrae were various, and the Haar-like features were only employed to represent the subject on the vertical, horizontal, or diagonal directions, we rotated the CT scout image seven times to make the vertebrae roughly horizontal in least one of the rotated images. Then, we employed Adaboost learning algorithm to construct a strong classifier for the vertebra detection by use of Haar-like features, and combined the detection results with the overlapping region according to the number of times they were detected. Finally, most of the false positives were removed by use of the contextual relationship between them. The detection scheme was evaluated on a database with 76 CT scout image. Our detection scheme reported 1.65 false positives per image at a sensitivity of 94.3% for initial detection of vertebral candidates, and then the performance of detection was improved to 0.95 false positives per image at a sensitivity of 98.6% for the further steps of false positive reduction. The proposed scheme achieved a high performance for the detection of vertebrae with different orientations.
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