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Colorectal cancer (CRC) is a common and lethal disease. Globally, CRC is the third most commonly diagnosed cancer in males and the second in females. For colorectal cancer, the best screening test available is the colonoscopy. During a colonoscopic procedure, a tiny camera at the tip of the endoscope generates a video of the internal mucosa of the colon. The video data are displayed on a monitor for the physician to examine the lining of the entire colon and check for colorectal polyps. Detection and removal of colorectal polyps are associated with a reduction in mortality from colorectal cancer. However, the miss rate of polyp detection during colonoscopy procedure is often high even for very experienced physicians. The reason lies in the high variation of polyp in terms of shape, size, textural, color and illumination. Though challenging, with the great advances in object detection techniques, automated polyp detection still demonstrates a great potential in reducing the false negative rate while maintaining a high precision. In this paper, we propose a novel anchor free polyp detector that can localize polyps without using predefined anchor boxes. To further strengthen the model, we leverage a Context Enhancement Module and Cosine Ground truth Projection. Our approach can respond in real time while achieving state-of-the-art performance with 99.36% precision and 96.44% recall.
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Automatic colorectal polyp detection in colonoscopy video is a fundamental task, which has received a lot of attention. Manually annotating polyp region in a large scale video dataset is time-consuming and expensive, which limits the development of deep learning techniques. A compromise is to train the target model by using labeled images and infer on colonoscopy videos. However, there are several issues between the image-based training and video-based inference, including domain differences, lack of positive samples, and temporal smoothness. To address these issues, we propose an Image-video-joint polyp detection network (Ivy-Net) to address the domain gap between colonoscopy images from historical medical reports and real-time videos. In our Ivy-Net, a modified mixup is utilized to generate training data by combining the positive images and negative video frames at the pixel level, which could learn the domain adaptive representations and augment the positive samples. Simultaneously, a temporal coherence regularization (TCR) is proposed to introduce the smooth constraint on feature-level in adjacent frames and improve polyp detection by unlabeled colonoscopy videos. For evaluation, a new large colonoscopy polyp dataset is collected, which contains 3056 images from historical medical reports of 889 positive patients and 7.5-hour videos of 69 patients (28 positive). The experiments on the collected dataset demonstrate that our Ivy-Net achieves the state-of-the-art result on colonoscopy video.
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis and interventions. However, accurate and real-time segmentation of endoscopic images is extremely challenging due to its high operator dependence and high-definition image quality. To utilize automated methods in clinical settings, it is crucial to design lightweight models with low latency such that they can be integrated with low-end endoscope hardware devices. In this work, we propose NanoNet, a novel architecture for the segmentation of video capsule endoscopy and colonoscopy images. Our proposed architecture allows real-time performance and has higher segmentation accuracy compared to other more complex ones. We use video capsule endoscopy and standard colonoscopy datasets with polyps, and a dataset consisting of endoscopy biopsies and surgical instruments, to evaluate the effectiveness of our approach. Our experiments demonstrate the increased performance of our architecture in terms of a trade-off between model complexity, speed, model parameters, and metric performances. Moreover, the resulting model size is relatively tiny, with only nearly 36,000 parameters compared to traditional deep learning approaches having millions of parameters.
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizers dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.
Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://github.com/NJU- Jet/SR_Mobile_Quantization.
Polyps in the colon are widely known as cancer precursors identified by colonoscopy either related to diagnostic work-up for symptoms, colorectal cancer screening or systematic surveillance of certain diseases. Whilst most polyps are benign, the number, size and the surface structure of the polyp are tightly linked to the risk of colon cancer. There exists a high missed detection rate and incomplete removal of colon polyps due to the variable nature, difficulties to delineate the abnormality, high recurrence rates and the anatomical topography of the colon. In the past, several methods have been built to automate polyp detection and segmentation. However, the key issue of most methods is that they have not been tested rigorously on a large multi-center purpose-built dataset. Thus, these methods may not generalise to different population datasets as they overfit to a specific population and endoscopic surveillance. To this extent, we have curated a dataset from 6 different centers incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3446 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset curated by a team of computational scientists and expert gastroenterologists. This dataset has been originated as the part of the Endocv2021 challenge aimed at addressing generalisability in polyp detection and segmentation. In this paper, we provide comprehensive insight into data construction and annotation strategies, annotation quality assurance and technical validation for our extended EndoCV2021 dataset which we refer to as PolypGen.
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