A computer-aided detection (CAD) second reader of colorectal polyps can decrease the rate of missed polyps in actual colonoscopy procedures. Currently, regular screening of colorectal cancer (CRC) demands a colonoscopy procedure during which polyps are located and removed. Unfortunately, different investigations have reported 22%-28% of polyps and 20%-24% of adenomatous polyps are missed. The adenoma detection rate (ADR) is a colonoscopy quality indicator highly dependent on expert training, spent time, device withdrawal technique, colon preparation and procedure-dependent factors. Several approaches have improved ADR, namely image enhancement, advancements in endoscope design and developments of accessories. Recently, artificial intelligence (AI) has shown potential to aid the task of polyp detection. This paper introduces an automatic detection of polyps that localize hyperplastic and adenomatous colorectal polyps in colonoscopy images and full video sequences. The proposed pipeline is achieved by two sequentially encoder-decoder Convolutional Neural Networks: The first detects frames with high probability of having polyps and the second estimates the actual location of the polyp. Detection of polyps showed an Annotated Area Covered AAC = 0.889 and IoU = 0.816 in actual colonoscopy images containing at least a polyp. In addition, in colonoscopy videos achieved a 0.63, 0.85, 0.65 of precision, specificity, and F1-score respectively for the ASU-Mayo database.
Colorectal cancer (CRC) is a major public health issue by its high incidence and mortality rate. CRC appears as premalignant lesions growing in the endoluminal wall, called polyps. Currently, a regular screening of CRC during a colonoscopy is the standard procedure to localize and treat polyps. However, evidence suggests 20% - 24% of adenomatous polyps may be missed during a routine colonoscopy. A limited adenoma detection (ADRs) is obtained because colon exploration is a very challenging task: it is highly dependent on the expert training and colon preparation. Hence, a second reader is required to support CRC screening. This paper presents a novel automatic computer-aided method to localize polyps in colonoscopy images. The method starts by segmenting an input frame into a set of superpixels, each of them characterized by concatenating color, texture, and shape features computed either locally, i.e., basic local statistics, or regionally, i.e., any measure is modulated by information in neighboring superpixels. Afterward, this representation feeds a classifier which sets a probability and a polyp is a group of superpixels with high assigned probability. Finally, the resultant groups were enclosed by a bounding box which corresponds to the colorectal polyp localization. The proposed approach was trained with 200 polyps (350 images) and tested with 86 polyps (236 images) of different size. Performance of our method was compared with a baseline based on deep CNN obtaining an average of Annotated Area Covered of 0.90 vs 0.89 and a precision of 0.96 vs 0.95 respectively.
New evidence suggests 25% - 26% of colon polyps may be missed during a routine colonoscopy[1, 2, 3, 4, 5]. These polyps or hyperplastic lesions are currently considered as pre-neoplastic lesions that must be detected. In this context, automatic strategies are appealing as second readers or diagnostic supporting tools. However, this task is challenging because of the huge variability and multiple sources of noise. This paper introduces a strategy for automatic detection of polyps larger than 5 mm. The underlying idea is that polyps in a sequence of frames are those locations with smaller frame-to-frame variance. The method starts by segmenting an input frame into a set of superpixels, i.e., clusters of neighbor pixels with minimal luminance variance. Each of these superpixels in characterized by a concatenated vector of 57 features collecting texture, shape, and color. A Support Vector Machine with a linear and Radial Basis Function (RBF) kernel was used as a supervised learning model. The evaluation was carried out using a set of 39 cases belonging to two datasets (6.594 frames: 3.123 with polyps and 3.471 without polyps) under a Leave-One-Out Cross Validation scheme and obtaining a 0.73 of accuracy. In addition, the data set was split into 70%-30% between train and test respectively and obtaining a 0.87 of accuracy.
KEYWORDS: Fetus, Signal detection, Wavelets, Electrocardiography, Electronic filtering, Independent component analysis, Discrete wavelet transforms, Detection and tracking algorithms, Data modeling, Signal to noise ratio
Non-invasive fetal electrocardiography (fECG) has attracted the medical community because of the importance of fetal monitoring. However, its implementation in clinical practice is challenging: the fetal signal has a low Signal- to-Noise-Ratio and several signal sources are present in the maternal abdominal electrocardiography (AECG). This paper presents a novel method to detect the fetal signal from a multi-channel maternal AECG. The method begins by applying filters and signal detrending the AECG signals. Afterwards, the maternal QRS complexes are identified and subtracted. The residual signals are used to detect the fetal QRS complex. Intervals of these signals are analyzed by using a wavelet decomposition. The resulting representation feds a previously trained Random Forest (RF) classifier that identifies signal intervals associated to fetal QRS complex. The method was evaluated on a public available dataset: the Physionet2013 challenge. A set of 50 maternal AECG records were used to train the RF classifier. The evaluation was carried out in signals intervals extracted from additional 25 maternal AECG. The proposed method yielded an 83:77% accuracy in the fetal QRS complex classification task.
A first diagnosis of colorectal cancer is performed by examination of polyp shape and appearance during an endoscopy routine procedure. However, the video-endoscopy is highly noisy because exacerbated physiological conditions like increased motility or secretion may limit the visual analysis of lesions. In this work a 3D reconstruction of the digestive tract is proposed, facilitating the polyp shape evaluation by highlighting its surface geometry and allowing an analysis from different perspectives. The method starts by a spatio-temporal map, constructed to group the different regions of the tract by their similar dynamic patterns during the sequence. Then, such map was convolved with a second derivative of a Gaussian kernel that emulates the camera distortion and allows to highlight the polyp surface. The position initialization in each frame of the kernel was computed from expert manual delineation and propagated along the sequence based on. Results show reliable reconstructions, with a salient 3D polyp structure that can then be better observed.
Polyp size quanti cation is currently the main variable for deciding the patient treatment during an endoscopic procedure. Nowadays, the polyp size is estimated by an expert, even when using devices that are provided with calibrated grids. As such estimation is highly subjective, automatic approaches have come to be appealing but also challenging because the polyp shape and appearance variability, the di erent types of motion present during the capture and the specular highlight noise. This work presents a novel approach to automatically estimate gastrointestinal polyp shapes in a video endoscopic sequence using spatiotemporal information. For doing so, a local spatio temporal descriptor is built up to obtain an initial segmentation since the polyp is the region with more movement. Then, an initial polyp manual segmentation outlines a region of interest (RoI) in the rst frame of the sequence and used as a reference for the polyp tracking during the sequence. Afterward, an exhaustive cross-correlation of the initial shape is carried out along the sequence and fused with the motion descriptor to re ne the original segmentation. The proposed approach was evaluated in 15 real video sequences achieving an average DSC score of 0:67% .
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