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.
KEYWORDS: Breast, 3D modeling, Breast cancer, Image segmentation, Tumor growth modeling, Ultrasonography, Mammography, Visualization, 3D image processing, Tumors, Ultrasound real time imaging
Breast cancer is the most common form of invasive cancer in women. In recent years, it has become standard practise to perform breast mass evaluations using ultrasound (US) imaging. US can accurately distinguish between malignant and benign breast masses when used by skilled radiologists, as compared to other medical imaging modalities such as MRI. Human domain knowledge is difficult to incorporate into the diagnosis of breast tumours because it differs greatly from person to person in terms of shape, border, curve, intensity, and other commonly used medical priors. A deep learning model that incorporates visual saliency can now be used to segment breast tumours in ultrasound images. Radiologists use the term "visual saliency," which refers to areas of an image that are more likely to be noticed. Features that prioritise spatial regions with high saliency levels are learned using the proposed method. According to validation results, tumours are more accurately identified in models that include attention layers than those without them. The salient attention model has the potential to improve medical image analysis accuracy and robustness by allowing deep learning architectures to incorporate task-specific knowledge. AUC-ROC plots show that our new model is more accurate in terms of IOU and AUC-ROC scores, dice score, precision, recall, and IOU.
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.
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