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.
The problems of the large variation in shape and location, and the complex background of many neighboring tissues in the pancreas segmentation hinder the early detection and diagnosis of pancreatic diseases. The U-Net family achieve great success in various medical image processing tasks such as segmentation and classification. This work aims to comparatively evaluate 2D U-Net, 2D U-Net++ and 2D U-Net3+ for CT pancreas segmentation. More interestingly, We also modify U-Net series in accordance with depth wise separable convolution (DWC) that replaces standard convolution. Without DWC, U-Net3+ works better than the other two networks and achieves an average dice similarity coefficient of 0.7555. Specifically, according to this study, we find that U-Net plus a simple module of DWC certainly works better than U-Net++ using redesigned dense skip connections and U-Net3+ using full-scale skip connections and deep supervision and can obtain an average dice similarity coefficient of 0.7613. More interestingly, the U-Net series plus DWC can significantly reduce the amount of training parameters from (39.4M, 47.2M, 27.0M) to (14.3M, 18.4M, 3.15M), respectively. At the same time, they also improve the dice similarity compared to using normal convolution.
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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