Monocular depth estimation is a popular task. Due to the difficulty of obtaining true depth labels for the bronchus and the characteristics of the bronchial image such as scarcity of texture, smoother surfaces and more holes, there are many challenges in bronchial depth estimation. Hence, we propose to use a ray tracing algorithm to generate virtual images along with their corresponding depth maps to train an asymmetric encoder-decoder transformer network for bronchial depth estimation. We propose the edge-aware unit to enhance the awareness of the bronchial internal structure considering that the bronchus has few texture features and many edges and holes. And asymmetric encoder-decoder is proposed by us for multi-layer features fusion. The experimental results of the virtual bronchial demonstrate that our method achieves the best results in several metrics, including MAE of 0.915 ± 0.596 and RMSE of 1.471 ± 1.097.
To deal with multitask segmentation, detection and classification of colon polyps, and solve the clinical problems of small polyps with similar background, missed detection and difficult classification, we have realized the method of supporting the early diagnosis and correct treatment of gastrointestinal endoscopy on the computer. We apply the residual U-structure network with image processing to segment polyps, and a Dynamic Attention Deconvolutional Single Shot Detector (DAD-SSD) to classify various polyps on colonic narrow-band images. The residual U-structure network is a two-level nested U-structure that is able to capture more contextual information, and the image processing improves the segmentation problem. DAD-SSD consists of Attention Deconvolutional Module (ADM) and Dynamic Convolutional Prediction Module (DCPM) to extract and fuse context features. We evaluated narrow-band images, and the experimental results validate the effectiveness of the method in dealing with such multi-task detection and classification. Particularly, the mean average precision (mAP) and accuracy are superior to other methods in our experiment, which are 76.55% and 74.4% respectively.
Kidney segmentation is fundamental for accurate diagnosis and treatment of kidney diseases. Computed tomography urography imaging is commonly used for radiologic diagnosis of patients with urologic disease. Recently, 2D and 3D fully convolutional networks are widely employed for medical image segmentation. However, most 2D fully convolutional networks do not take inter-slice spatial information into consideration, resulting in incomplete and inaccurate segmentation of targets in 3D volumes. While the spatial information is truly important for 3D volumes segmentation. To tackle these problems, we propose a computed tomography urography kidney segmentation method on the basis of spatiotemporal fully convolutional networks that employ the convolutional long short-term memory network to model inter-slice features of computed tomography urography images. We trained and tested our proposed method on kidney computed tomography urography data. The experimental results demonstrate our proposed method can effectively leverage the inter-slice spatial information to achieve better (or comparable) results than current 2D and 3D fully convolutional networks.
Abdominal kidney segmentation plays an essential role in diagnosis and treatment of kidney diseases, particularly in surgical planning and clinical outcome analysis before and after kidney surgery. It still remains challenging to precisely segment the kidneys from CT images. Current segmentation approaches still suffer from CT image noises and variations caused by different CT scans, kidney location discrepancy, pathological morphological diversity among patients, and partial volume artifacts. This paper proposes a fully automatic kidney segmentation method that employs a volumetric convolution driven cascaded V-Net architecture and false positive reduction to precisely extract the kidney regions. We evaluate our method on publicly available kidney CT data. The experimental results demonstrate that our proposed method is a promising method for accurate kidney segmentation, providing a dice coefficient of 0.95 better than other approaches as well less computational time.
Endoscopic video sequences provide surgeons with much structural information (e.g., vessels and neurovascular bundles) that guides them to accurately manipulate various surgical tools and avoid surgical risks. Unfortunately, it is difficult for surgeons to intuitively perceive these small structures with tiny pulsation motion on endoscopic images. This work proposes a new endoscopic video motion magnification method to accurately generate the amplified pulsation motion that can be intuitively and easily visualized by surgeons. The proposed method explores a new temporal filtering for Eulerian motion magnification method to precisely magnify the tiny pulsation motion and simultaneously suppress noise and artifacts in endoscopic videos. We evaluate our approach on surgical endoscopic videos acquired in robotic prostatectomy. The experimental results demonstrate that our proposed temporal filtering method essentially outperforms other filters in current video motion magnification approaches, while it provides better visual quality and quantitative assessment than other methods.
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