35 research outputs found

    Automatic Liver Segmentation Using an Adversarial Image-to-Image Network

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    Automatic liver segmentation in 3D medical images is essential in many clinical applications, such as pathological diagnosis of hepatic diseases, surgical planning, and postoperative assessment. However, it is still a very challenging task due to the complex background, fuzzy boundary, and various appearance of liver. In this paper, we propose an automatic and efficient algorithm to segment liver from 3D CT volumes. A deep image-to-image network (DI2IN) is first deployed to generate the liver segmentation, employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep supervision. Then an adversarial network is utilized during training process to discriminate the output of DI2IN from ground truth, which further boosts the performance of DI2IN. The proposed method is trained on an annotated dataset of 1000 CT volumes with various different scanning protocols (e.g., contrast and non-contrast, various resolution and position) and large variations in populations (e.g., ages and pathology). Our approach outperforms the state-of-the-art solutions in terms of segmentation accuracy and computing efficiency.Comment: Accepted by MICCAI 201

    ГЕНЕРАЦИЯ ИСКУССТВЕННЫХ РЕНТГЕНОВСКИХ ИЗОБРАЖЕНИЙ ГРУДНОЙ КЛЕТКИ С ИСПОЛЬЗОВАНИЕМ ГЕНЕРАТИВНО-СОСТЯЗАТЕЛЬНЫХ НЕЙРОННЫХ СЕТЕЙ

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    This paper deals with the problem of generating artificial chest x-ray images which expected to be almost undistinguishable from real ones. Generation was performed using Generative Adversarial Nets (GAN). Similarity of resultant artificial images to the real ones was evaluated both by visual examination and by quantitative assessment using commonly known Local Binary Patterns. It was concluded that GANs can be successfully employed for generating realistically appearing artificial chest radiographs. However, an automatic procedure of selecting “most realistic” results is necessary for excluding the final visual quality control stage and making the whole generation process fully automatic.Рассматривается задача генерации правдоподобных (трудноотличимых от реальных) рентгеновских изображений грудной клетки человека в норме. Указанная задача решается с использованием генеративно-состязательных нейронных сетей (Generative Adversarial Nets). Степень правдоподобия получаемых результатов оценивается как визуально, так и количественно путем сравнения дескрипторов структуры изображений, основанных на локальных двоичных шаблонах

    End-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation

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    Automatic segmentation of abdomen organs using medical imaging has many potential applications in clinical workflows. Recently, the state-of-the-art performance for organ segmentation has been achieved by deep learning models, i.e., convolutional neural network (CNN). However, it is challenging to train the conventional CNN-based segmentation models that aware of the shape and topology of organs. In this work, we tackle this problem by introducing a novel end-to-end shape learning architecture -- organ point-network. It takes deep learning features as inputs and generates organ shape representations as points that located on organ surface. We later present a novel adversarial shape learning objective function to optimize the point-network to capture shape information better. We train the point-network together with a CNN-based segmentation model in a multi-task fashion so that the shared network parameters can benefit from both shape learning and segmentation tasks. We demonstrate our method with three challenging abdomen organs including liver, spleen, and pancreas. The point-network generates surface points with fine-grained details and it is found critical for improving organ segmentation. Consequently, the deep segmentation model is improved by the introduced shape learning as significantly better Dice scores are observed for spleen and pancreas segmentation.Comment: Accepted to International Workshop on Machine Learning in Medical Imaging (MLMI2019
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