Computer-aided detection systems for lung nodules play an important role in the early diagnosis and treatment process. False positive reduction is a significant component in pulmonary nodule detection. To address the visual similarities between nodules and false positives in CT images and the problem of two-class imbalanced learning, we propose a Central Attention Convolutional Neural Network on Imbalanced Data (CACNNID) to distinguish nodules from a large number of false positive candidates. To solve the imbalanced data problem, we consider density distribution, data augmentation, noise reduction, and balanced sampling for making the network well-learned. During the network training, we design the model to pay high attention to the central information and minimize the influence of irrelevant edge information for extracting the discriminant features. The proposed model has been evaluated on the public dataset LUNA16 and achieved a mean sensitivity of 92.64%, specificity of 98.71%, accuracy of 98.69%, and AUC of 95.67%. The experimental results indicate that our model can achieve satisfactory performance in false positive reduction.
Accurate segmentation of the prostate has many applications in the detection, diagnosis and treatment of prostate cancer. Automatic segmentation can be a challenging task because of the inhomogeneous intensity distributions on MR images. In this paper, we propose an automatic segmentation method for the prostate on MR images based on anatomy. We use the 3D U-Net guided by anatomy knowledge, including the location and shape prior knowledge of the prostate on MR images, to constrain the segmentation of the gland. The proposed method has been evaluated on the public dataset PROMISE2012. Experimental results show that the proposed method achieves a mean Dice similarity coefficient of 91.6% as compared to the manual segmentation. The experimental results indicate that the proposed method based on anatomy knowledge can achieve satisfactory segmentation performance for prostate MRI.
Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.
The contrast enhancement of tumor regions in medical images can improve the performance of tumor detection, segmentation, and diagnosis. However, the main existing enhancement methods aim to enhance the contrast and the resolution on the whole image, instead of highlighting the lesion regions. The blurry edges lead to the difficulty of distinguishing the tumor from the healthy tissues accurately. This issue can be hardly solved by those global enhancement methods. In this paper, we focus on the local enhancement and propose a novel deep learning-based approach called U-SDRC to enhance the contrast between tumor regions and surrounding background tissues to make the tumor regions distinguishable. We introduce a U-net deep network to tackle this problem and present a novel SDRC loss function to achieve the goal of enhancing tumor lesions and simultaneously preserving the original appearance of other regions in the image. We evaluate our approach on a clinical dataset that comprises 1394 liver CT slices. The encouraging experimental results show that the proposed method can lead to a good visual enhancement effect and bring improvements to medical tasks such as tumor segmentation and diagnosis.
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