Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

U-Net and its variants for medical image segmentation: theory and applications

U-net is an image segmentation technique developed primarily for medical image analysis that can precisely segment images using a scarce amount of training data. These traits provide U-net with a very high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in all major image modalities from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. As the potential of U-net is still increasing, in this review we look at the various developments that have been made in the U-net architecture and provide observations on recent trends. We examine the various innovations that have been made in deep learning and discuss how these tools facilitate U-net. Furthermore, we look at image modalities and application areas where U-net has been applied.Comment: 42 pages, in IEEE Acces

Efficient evolutionary-based neural architecture search in few GPU hours for image classification and medical image segmentation

Orientador: Lucas Ferrari de OliveiraTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 20/09/2021Inclui referências: p. 132-139Área de concentração: Ciência da ComputaçãoResumo: O uso de aprendizagem profunda (AP) está crescendo rapidamente, já que o poder computacional atual fornece otimização e inferência rápidas. Além disso, vários métodos exclusivos de AP estão evoluindo, permitindo resultados superiores em visão computacional, reconhecimento de voz e análise de texto. Os métodos AP extraem característica automaticamente para melhor representação de um problema específico, removendo o árduo trabalho do desenvolvimento de descritores de características dos métodos convencionais. Mesmo que esse processo sejaautomatizado, a criação inteligente de redes neurais é necessária para o aprendizado adequado da representação, o que requer conhecimento em AP. O campo de busca de arquiteturas neurais (BAN) foca no desenvolvimento de abordagens inteligentes que projetam redes robustas automaticamente para reduzir o conhecimento exigido para o desenvolvimento de redes eficientes. BAN pode fornecer maneiras de descobrir diferentes representações de rede, melhorando o estado da arte em diferentes aplicações. Embora BAN seja relativamente nova, várias abordagens foram desenvolvidas para descobrir modelos robustos. Métodos eficientes baseados em evolução são amplamente populares em BAN, mas seu alto consumo de placa gráfica (de alguns dias a meses)desencoraja o uso prático. No presente trabalho, propomos duas abordagens BAN baseadas na evolução eficiente com baixo custo de processamento, exigindo apenas algumas horas de processamento na placa gráfica (menos de doze em uma RTX 2080Ti) para descobrir modelos competitivos. Nossas abordagens extraem conceitos da programação de expressão gênica para representar e gerar redes baseadas em células robustas combinadas com rápido treinamento de candidatos, compartilhamento de peso e combinações dinâmicas. Além disso, os métodos propostos são empregados em um espaço de busca mais amplo, com mais células representando uma rede única. Nossa hipótese central é que BAN baseado na evolução pode ser usado em uma busca com baixo custo (combinada com uma estratégia robusta e busca eficiente) em diversas tarefas de visão computacional sem perder competitividade. Nossos métodos são avaliados em diferentes problemas para validar nossa hipótese: classificação de imagens e segmentação semântica de imagens médicas. Para tanto, as bases de dados CIFAR são estudadas para atarefa de classificação e o desafio CHAOS para a tarefa de segmentação. As menores taxas de erro encontradas nas bases CIFAR-10 e CIFAR-100 foram 2,17% ± 0,10 e 15,47% ± 0,51,respectivamente. Quanto às tarefas do desafio CHAOS, os valores de Dice ficaram entre 90% e96%. Os resultados obtidos com nossas propostas em ambas as tarefas mostraram a descoberta de redes robustas para ambas as tarefas com baixo custo na fase de busca, sendo competitivas em relação ao estado da arte em ambos os desafios.Abstract: Deep learning (DL) usage is growing fast since current computational power provides fast optimization and inference. Furthermore, several unique DL methods are evolving, enabling superior computer vision, speech recognition, and text analysis results. DL methods automatically extract features to represent a specific problem better, removing the hardworking of feature engineering from conventional methods. Even if this process is automated, intelligent network design is necessary for proper representation learning, which requires expertise in DL. The neural architecture search (NAS) field focuses on developing intelligent approaches that automatically design robust networks to reduce the expertise required for developing efficient networks. NAS may provide ways to discover different network representations, improving the state-of-the-art indifferent applications. Although NAS is relatively new, several approaches were developed for discovering robust models. Efficient evolutionary-based methods are widely popular in NAS, buttheir high GPU consumption (from a few days to months) discourages practical use. In the presentwork, we propose two efficient evolutionary-based NAS approaches with low-GPU cost, requiring only a few GPU hours (less than twelve in an RTX 2080Ti) to discover competitive models. Our approaches extract concepts from gene expression programming to represent and generate robust cell-based networks combined with fast candidate training, weight sharing, and dynamic combinations. Furthermore, the proposed methods are employed in a broader search space, withmore cells representing a unique network. Our central hypothesis is that evolutionary-based NAScan be used in a low-cost GPU search (combined with a robust strategy and efficient search) indiverse computer vision tasks without losing competitiveness. Our methods are evaluated indifferent problems to validate our hypothesis: image classification and medical image semantic segmentation. For this purpose, the CIFAR datasets are studied for the classification task andthe CHAOS challenge for the segmentation task. The lowest error rates found in CIFAR-10 andCIFAR-100 datasets were 2.17% ± 0.10 and 15.47% ± 0.51, respectively. As for the CHAOS challenge tasks, the dice scores were between 90% and 96%. The obtained results from our proposal in both tasks shown the discovery of robust networks for both tasks with little GPU costin the search phase, being competitive to state-of-the-art approaches in both challenges

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Blurry Boundary Delineation and Adversarial Confidence Learning for Medical Image Analysis

Low tissue contrast and fuzzy boundaries are major challenges in medical image segmentation which is a key step for various medical image analysis tasks. In particular, blurry boundary delineation is one of the most challenging problems due to low-contrast and even vanishing boundaries. Currently, encoder-decoder networks are widely adopted for medical image segmentation. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve more accurate segmentation performance. However, in many applications (e.g., images with blurry boundaries), these models often cannot precisely locate complex boundaries and segment tiny isolated parts. To solve this challenging problem, we empirically analyze why simple lateral connections in encoder-decoder architectures are not able to accurately locate indistinct boundaries. Based on the analysis, we argue learning high-resolution semantic information in the lateral connection can better delineate the blurry boundaries. Two methods have been proposed to achieve such a goal. a) A high-resolution pathway composed of dilated residual blocks has been adopted to replace the simple lateral connection for learning the high-resolution semantic features. b) A semantic-guided encoder feature learning strategy is further proposed to learn high-resolution semantic encoder features so that we can more accurately and efficiently locate the blurry boundaries. Besides, we also explore a contour constraint mechanism to model blurry boundary detection. Experimental results on real clinical datasets (infant brain MRI and pelvic organ datasets) show that our proposed methods can achieve state-of-the-art segmentation accuracy, especially for the blurry regions. Further analysis also indicates that our proposed network components indeed contribute to the performance gain. Experiments on an extra dataset also validate the generalization ability of our proposed methods. Generative adversarial networks (GANs) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is usually directly applied to the original supervised segmentation (synthesis) networks. The use of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot be improved as much as the qualitative performance, and it can even become worse in some cases. In this dissertation, I explore how adversarial learning could be more useful in supervised segmentation (synthesis) models, i.e., how to synchronously improve visual and quantitative performance. I first analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, an adversarial confidence learning framework is proposed for taking better advantage of adversarial learning; that is, besides the adversarial learning for emphasizing visual perception, the confidence information provided by the adversarial network is utilized to enhance the design of the supervised segmentation (synthesis) network. In particular, I propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. Furthermore, various loss functions of GANs are investigated and the binary cross entropy loss is finally chosen to train the proposed adversarial confidence learning system so that the modeling capacity of the discriminator is retained for confidence learning. With these settings, two machine learning algorithms are proposed to solve some specific medical image analysis problems. a) A difficulty-aware attention mechanism is proposed to properly handle hard samples or regions by taking structural information into consideration so that the irregular distribution of medical data could be appropriately dealt with. Experimental results on clinical and challenge datasets show that the proposed algorithm can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can synchronously improve the visual perception and quantitative performance. b) A semisupervised segmentation model is proposed to alleviate the everlasting challenge for medical image segmentation - lack of annotated data. The proposed method can automatically recognize well-segmented regions (instead of the entire sample) and dynamically include them to increase the label set during training. Specifically, based on the confidence map, a region-attention based semi-supervised learning strategy is designed to further train the segmentation network. Experimental results on real clinical datasets show that the proposed approach can achieve better segmentation performance with extra unannotated data.Doctor of Philosoph