DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models

A Medical Analysis for Colorectal Lymphomas using 3D MRI Images and Deep Residual Boltzmann CNN Mechanism

In this technological world the healthcare is very crucial and difficult to spend time for the wellbeing. The lifestyle disease can transform in to the life threating disease and lead to critical stages. Colorectal lymphomas are the 3rd most malignancy death in the entire world. The estimation of the volume of lymphomas is often used by Magnetic Resonance Imaging during medical diagnosis, particularly in advanced stages. The research study can be classified in multiple stages. In the initial stages, an automated method is used to calculated the volume of the colorectal lymphomas using 3D MRI images. The process begins with feature extraction using Iterative Multilinear Component Analysis and Multiscale Phase level set segmentation based on CNN model. Then, a logical frustum model is utilized for 3D simulation of colon lymphoma for rendering the medical data. The next stages is focused on tackling the matter of segmentation and classification of abnormality and normality of lymph nodes. A semi supervised fuzzy logic algorithm for clustering is used for segmentation, whereas bee herd optimization algorithm with scale down for employed to intensify corresponding classifier rate of detection. Finally, classification is performed using Deep residual Boltzmann CNN. Our proposed methodology gives a better results and diagnosis prediction for lymphomas for an accuracy 97.7%, sensitivity 95.7% and specify as 95.8% which is superior than the traditional approach

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. The authors would also like to thank Dr. Federico Soria for his support on this manuscript and Dr. José Carlos Marín, from Hospital 12 de Octubre, and Dr. Ángel Calderón and Dr. Francisco Polo, from Hospital de Basurto, for the images in Fig. 4

Polyp Segmentation in Colonoscopy Images with Convolutional Neural Networks

The thesis looks at approaches to segmentation of polyps in colonoscopy images. The aim was to investigate and develop methods that are robust, accurate and computationally efficient and which can compete with the current state-of-the-art in polyp segmentation. Colorectal cancer is one of the leading cause of cancer deaths worldwide. To decrease mortality, an assessment of polyp malignancy is performed during colonoscopy examination so polyps can be removed at an early stage. In current routine clinical practice, polyps are detected and delineated manually in colonoscopy images by highly trained clinicians. To automate these processes, machine learning and computer vision techniques have been utilised. They have been shown to improve polyp detectability and segmentation objectivity. However, polyp segmentation is a very challenging task due to inherent variability of polyp morphology and colonoscopy image appearance. This research considers a range of approaches to polyp segmentation – seeking out those that offer a best compromise between accuracy and computational complexity. Based on analysis of existing machine learning and polyp image segmentation techniques, a novel hybrid deep learning segmentation method is proposed to alleviate the impact of the above stated challenges on polyp segmentation. The method consists of two fully convolutional networks. The first proposed network is based on a compact architecture with large receptive fields and multiple classification paths. The method performs well on most images, accurately segmenting polyps of diverse morphology and appearance. However, this network is prone to misdetection of very small polyps. To solve this problem, a second network is proposed, which primarily aims to improve sensitivity to small polyp details by emphasising low-level image features. In order to fully utilise information contained in the available training dataset, comprehensive data augmentation techniques are adopted. To further improve the performance of the proposed segmentation methods, test-time data augmentation is also implemented. A comprehensive multi-criterion analysis of the proposed methods is provided. The result demonstrates that the new methodology has better accuracy and robustness than the current state-of-the-art, as proven by the outstanding performance at the 2017 and 2018 GIANA polyp segmentation challenges