In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices. Experiments show that the PSNR of the proposed method is increased by 10dB compared to the direct FDK reconstruction and almost 3dB compared to the modified original Primal-Dual Network when using only 23 projections. The presented network is not optimized wrt. memory consumption or hyperparameters but merely serves as a proof of concept and is limited to low resolution projections and volumes.
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Human Technopole（人类技术园）；German Centre for Neurodegenerative Disease（德国神经退行性疾病中心）；Centre for Behavioural Brain Sciences（行为脑科学中心）
Computed tomography and magnetic resonance imaging are two widely used clinical imaging modalities for non-invasive diagnosis. However, both of these modalities come with certain problems. CT uses harmful ionising radiation, and MRI suffers from slow acquisition speed. Both problems can be tackled by undersampling, such as sparse sampling. However, such undersampled data leads to lower resolution and introduces artefacts. Several techniques, including deep learning based methods, have been proposed to reconstruct such data. However, the undersampled reconstruction problem for these two modalit…
Human veins are important for carrying the blood from the body-parts to the heart. The improper functioning of the human veins may arise from several venous diseases. Varicose vein is one such disease wherein back flow of blood can occur, often resulting in increased venous pressure or restricted blood flow due to changes in the structure of vein. To examine the functional characteristics of the varicose vein, it is crucial to study the physical and bio mechanical properties of the vein. This work proposes a segmentation model Opto-UNet, for segmenting the venous wall structure. Optical Cohere…
University of Pittsburgh School of Medicine（匹兹堡大学医学院）；VA Pittsburgh Healthcare System（匹兹堡退伍军人医疗系统）
Purpose: To develop and validate a computer tool for automatic and simultaneous segmentation of body composition depicted on computed tomography (CT) scans for the following tissues: visceral adipose (VAT), subcutaneous adipose (SAT), intermuscular adipose (IMAT), skeletal muscle (SM), and bone. Approach: A cohort of 100 CT scans acquired from The Cancer Imaging Archive (TCIA) was used - 50 whole-body positron emission tomography (PET)-CTs, 25 chest, and 25 abdominal. Five different body compositions were manually annotated (VAT, SAT, IMAT, SM, and bone). A training-while-annotating strategy w…
Simon Fraser University（西蒙弗雷泽大学）；Tencent YouTu Lab（腾讯优图实验室）
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is widely used for the diagnosis of ophthalmic diseases with retinal alteration, such as layer deformation and fluid accumulation. In this paper, a novel framework was proposed to segment retinal layers with fluid presence. The main contribution of this study is two folds: 1) we developed a cascaded network framework to incorporate the prior structural knowledge; 2) we proposed a novel deep neural network based on U-Net an…
Comparative Study of UNet-based Architectures for Liver Tumor Segmentation in Multi-Phase Contrast-Enhanced Computed Tomography
School of Computer Science and Engineering（计算机科学与工程学院）；The Saigon International University（暹罗国际大学）；School of Biomedical Engineering（生物医学工程学院）；International University（国际大学）；Vietnam National University HCMC（越南国家大学胡志明市分校）
George Mason University（乔治梅森大学）；The MITRE Corporation（MITRE公司）
In photoacoustic tomography (PAT), the acoustic pressure waves produced by optical excitation are measured by an array of detectors and used to reconstruct an image. Sparse spatial sampling and limited-view detection are two common challenges faced in PAT. Reconstructing from incomplete data using standard methods results in severe streaking artifacts and blurring. We propose a modified convolutional neural network (CNN) architecture termed Dense Dilation UNet (DD-UNet) for correcting artifacts in 3D PAT. The DD-Net leverages the benefits of dense connectivity and dilated convolutions to impro…
WAVE-UNET: Wavelength based Image Reconstruction method using attention UNET for OCT images
Institute of Optical Materials and Technologies, Bulgarian Academy of Sciences（保加利亚科学院光学材料与技术研究所）；Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与通信科学学院）；Electronics and Telecommunications Research Institute（电子与电信研究所）
In this work, we investigate the application of deep learning methods for computed tomography in the context of having a low-data regime. As motivation, we review some of the existing approaches and obtain quantitative results after training them with different amounts of data. We find that the learned primal-dual has an outstanding performance in terms of reconstruction quality and data efficiency. However, in general, end-to-end learned methods have two issues: a) lack of classical guarantees in inverse problems and b) lack of generalization when not trained with enough data. To overcome the…
CAD-Unet: A Capsule Network-Enhanced Unet Architecture for Accurate Segmentation of COVID-19 Lung Infections from CT Images
School of Information Engineering, Ningxia University（宁夏大学信息工程学院）；Ningxia Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West, Ningxia University（宁夏人工智能与信息安全重点实验室）；School of Mathematics and Computer Science, Ningxia Normal University（宁夏师范学院数学与计算机科学学院）；School of Advanced Interdisciplinary Studies, Ningxia University（宁夏大学先进交叉学科学院）
Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scans with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similari…
Vidyasirimedhi Institute of Science & Technology（维迪亚西里梅迪科技学院）；Kamnoetvidya Science Academy（卡姆诺维迪亚科学学院）；Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院）；Cixi Institute of Biomedical Engineering（慈溪生物医学工程研究所）；Kasetsart University（泰国农业大学）；Nanyang Technological University（南洋理工大学）
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the data annotation process on the curvilinear structure like the retinal vasculature is very costly and time consuming, albeit few tried to address the annotation problem. In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation. The proposed method, called OCTAve, combines the weakly-supervised learning using scribble-annotated ground truth …
Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especially challenging to train the reconstruction networks for three-dimensional computed tomography (CT) because of the high resolution of CT images. The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstruc…
Otto-von-Guericke University（奥托·冯·格里克大学）；University Hospital of Magdeburg（马格德堡大学医院）
Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. T…
University of Edinburgh（爱丁堡大学）；ETH Zurich（苏黎世联邦理工学院）
Spectral Photon-Counting Computed Tomography (SPCCT) is a promising technology that has shown a number of advantages over conventional X-ray Computed Tomography (CT) in the form of material separation, artefact removal and enhanced image quality. However, due to the increased complexity and non-linearity of the SPCCT governing equations, model-based reconstruction algorithms typically require handcrafted regularisation terms and meticulous tuning of hyperparameters making them impractical to calibrate in variable conditions. Additionally, they typically incur high computational costs and in ca…
Gordon Center for Medical Imaging, Massachusetts General Hospital（麻省总医院戈登医学成像中心）；Harvard Medical School（哈佛医学院）；Beijing International Center for Mathematical Research, Peking University（北京大学北京国际数学研究中心）
Computer aided diagnostic (CAD) system is crucial for modern med-ical imaging. But almost all CAD systems operate on reconstructed images, which were optimized for radiologists. Computer vision can capture features that is subtle to human observers, so it is desirable to design a CAD system op-erating on the raw data. In this paper, we proposed a deep-neural-network-based detection system for lung nodule detection in computed tomography (CT). A primal-dual-type deep reconstruction network was applied first to convert the raw data to the image space, followed by a 3-dimensional convolutional ne…
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT
Shenzhen University General Hospital（深圳大学总医院）；College of Mechatronics and Engineering, Shenzhen University（深圳大学机电与工程学院）；College of Electronics and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）；Research Center of Medical Plasma Technology, Shenzhen University（深圳大学医用等离子体技术研究中心）；Shenzhen Lihuy Medical Tech. co. Ltd.（深圳利护医疗科技有限公司）
Expanding the Medical Decathlon dataset: segmentation of colon and colorectal cancer from computed tomography images
Neural Network Technologies Center（神经网络技术中心）；Institute of Urology and Human Reproductive Health（泌尿学与人类生殖健康研究所）；N.E. Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）；I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University)（俄罗斯联邦卫生部I.M.谢切诺夫第一莫斯科国立医科大学（谢切诺夫大学））；Hospital of Faculty Surgery No. 2, UKB No. 4（第4大学临床医院第二外科教学医院）；University Clinical Hospital No. 2（第二大学临床医院）
本文通过为 Medical Decathlon 数据集增加经放射科医生验证的结直肠癌 CT 分割标记，并训练 UNet 模型进行 5 折交叉验证，以提升结直肠癌自动分割与早期检测能力。
The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution. By harnessing a generative adversarial network, we can synthesize a massive amount of physics-based X-ray images, so-called Variationally Reconstructed Radiographs (VRRs), alongside their segmentation from more accurate labeled 3D Computed Tomography data. As …
Multi-target and multi-stage liver lesion segmentation and detection in multi-phase computed tomography scans
King Fahd University of Petroleum and Minerals（法赫德国王石油矿产大学）；Vinbrain JSC（Vinbrain股份公司）；Le Quy Don Technical University（黎贵敦技术大学）；University Medical Center at Ho Chi Minh City（胡志明市大学医疗中心）
Recent advances in reconstruction methods for inverse problems leverage powerful data-driven models, e.g., deep neural networks. These techniques have demonstrated state-of-the-art performances for several imaging tasks, but they often do not provide uncertainty on the obtained reconstruction. In this work, we develop a scalable, data-driven, knowledge-aided computational framework to quantify the model uncertainty via Bayesian neural networks. The approach builds on, and extends deep gradient descent, a recently developed greedy iterative training scheme, and recasts it within a probabilistic…
Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning …
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance…
Triggering hallucinations in model-based MRI reconstruction via adversarial perturbations
Faculty of Engineering and Architecture/Computer Engineering（工程与建筑学院/计算机工程学院）；Department of Computer Science, Mathematics and Statistics（计算机科学、数学与统计学系）；Ghent University（根特大学）；Data Mining and Modeling for Biomedicine Group（生物医学数据挖掘与建模小组）；VIB Inflammation Research Center（VIB炎症研究中心）
Zhejiang University（浙江大学）；University of Florida（佛罗里达大学）
Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatial-temporal convolutional primal dual network (STPDnet) for dynamic PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability. The experiments of real rat scan data have shown that the proposed method can achie…
Positron Emission Tomography (PET) is an essential technique in many clinical applications that allows for quantitative imaging at the molecular level. This study aims to develop a denoising method using novel dilated convolutional neural network to recover full-count images from low-count images. We adopted similar hierarchal structure from the conventional uNet and incorporated dilated kernels in each convolution to allow the network to observe larger, and perhaps, more robust, features within the image. Our dNet were trained alongside a uNet for comparison. Our 2.5D model used a training se…
We propose a new type of efficient deep-unrolling networks for solving imaging inverse problems. Conventional deep-unrolling methods require full forward operator and its adjoint across each layer, and hence can be significantly more expensive computationally as compared with other end-to-end methods that are based on post-processing of model-based reconstructions, especially for 3D image reconstruction tasks. We develop a stochastic (ordered-subsets) variant of the classical learned primal-dual (LPD), which is a state-of-the-art unrolling network for tomographic image reconstruction. The prop…
University of Iowa（爱荷华大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of Utah（犹他大学）
Local tissue expansion of the lungs is typically derived by registering computed tomography (CT) scans acquired at multiple lung volumes. However, acquiring multiple scans incurs increased radiation dose, time, and cost, and may not be possible in many cases, thus restricting the applicability of registration-based biomechanics. We propose a generative adversarial learning approach for estimating local tissue expansion directly from a single CT scan. The proposed framework was trained and evaluated on 2500 subjects from the SPIROMICS cohort. Once trained, the framework can be used as a registr…
University of Iowa（爱荷华大学）；Beijing Institute of Ophthalmology（北京眼科研究所）；Beijing Tongren Hospital（北京同仁医院）；Capital University of Medical Science（首都医科大学）；Eye Hospital of China Academy of Chinese Medical Sciences（中国中医科学院眼科医院）；University of Notre Dame（圣母大学）；Heidelberg University（海德堡大学）
Segmentation of multiple surfaces in medical images is a challenging problem, further complicated by the frequent presence of weak boundary and mutual influence between adjacent objects. The traditional graph-based optimal surface segmentation method has proven its effectiveness with its ability of capturing various surface priors in a uniform graph model. However, its efficacy heavily relies on handcrafted features that are used to define the surface cost for the "goodness" of a surface. Recently, deep learning (DL) is emerging as powerful tools for medical image segmentation thanks to its su…
Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge.
Enabling self-supervised learned primal dual with Noise2Inverse
Research Unit of Mathematical Sciences, University of Oulu, Finland（奥卢大学数学科学研究中心，芬兰）；Department of Mathematics and Information Science, Josai University, Japan（立命馆大学数学与信息科学系，日本）；Department of Computer Science, University College London, United Kingdom（伦敦大学学院计算机科学系，英国）
The sparse-views x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. However, in particular, the reconstructed image usually suffers from complex artifacts and noise, when the sampling is insufficient. In order to deal such issue, a full-automatic reconstruction (FAR) net is proposed for sparse-views CT reconstruction via deep learning technique. Different with the usual network in deep learning reconstruction, the proposed neural network is an End-to-End network by which the image is predicted directly from projection data. The main challen…
In optical diffraction tomography (ODT), the three-dimensional scattering potential of a microscopic object rotating around its center is recovered by a series of illuminations with coherent light. Reconstruction algorithms such as the filtered backpropagation require knowledge of the complex-valued wave at the measurement plane, whereas often only intensities, i.e., phaseless measurements, are available in practice. We propose a new reconstruction approach for ODT with unknown phase information based on three key ingredients. First, the light propagation is modeled using Born's approximation …
We introduce the learned simultaneous iterative reconstruction technique (SIRT) for tomographic reconstruction. The learned SIRT algorithm is a deep learning based reconstruction method combining model knowledge with a learned component. The algorithm is trained by mapping raw measured data to the reconstruction results over several iterations. The Learned SIRT algorithm is applied to a cone beam geometry on a circular orbit, a challenging problem for learned methods due to its 3D geometry and its inherent inability to completely capture the patient anatomy. A comparison of 2D reconstructions …
Institut universitaire de France（法国大学研究院）；CHB Hospital（CHB医院）；University of Rouen Normandy（鲁昂诺曼底大学）
Lymphoma detection and segmentation from whole-body Positron Emission Tomography/Computed Tomography (PET/CT) volumes are crucial for surgical indication and radiotherapy. Designing automatic segmentation methods capable of effectively exploiting the information from PET and CT as well as resolving their uncertainty remain a challenge. In this paper, we propose an lymphoma segmentation model using an UNet with an evidential PET/CT fusion layer. Single-modality volumes are trained separately to get initial segmentation maps and an evidential fusion layer is proposed to fuse the two pieces of ev…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Helmholtz-Zentrum hereon（亥姆霍兹中心hereon）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）；Hannover Medical School（汉诺威医学院）
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours. The time separation technique (TST) has been successfully used for modelling C-arm cone-beam computed tomography (CBCT) perfusion data. The reconstruction can be accompanied by the segmentation of the liver - for better visualisation and for generating comprehensive perfusion maps. Recently introduced Turbolift learning has been seen to perform well while working with TST reconstructions, but has not been explored for the time-resolved volumes (TRV) estimated out of TST reconstructions. The segmentati…
In this paper we propose a motion-aware variational approach to reconstruct moving objects from sparse dynamic data. The motivation of this work stems from X-ray imaging of plants perfused with a liquid contrast agent, aimed at increasing the contrast of the images and studying the phloem transport in plants over time. The key idea of our approach is to deploy 3D shearlets as a space-temporal prior, treating time as the third dimension. The rationale behind this model is that a continuous evolution of a cartoon-like object is well suited for the use of 3D shearlets. We provide a basic mathemat…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；Helmholtz-Zentrum hereon（亥姆霍兹赫里昂中心）；Hannover Medical School（汉诺威医学院）；Human Technopole（人类技术园）
Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT data need to be segmented for proper visualisation and interpretation of perfusion maps. This research proposes Turbolift learning, which trains a modified version of the multi-scale Attention UNet on different liver segmentation tasks seri…
In this paper we consider the problem of image reconstruction in optoacoustic tomography. In particular, we devise a deep neural architecture that can explicitly take into account the band-frequency information contained in the sinogram. This is accomplished by two means. First, we jointly use a linear filtered back-projection method and a fully dense UNet for the generation of the images corresponding to each one of the frequency bands considered in the separation. Secondly, in order to train the model, we introduce a special loss function consisting of three terms: (i) a separating frequency…
Based on photoacoustic effect, photoacoustic tomography is developing very fast in recent years, and becoming an important imaging tool for both preclinical and clinical studies. With enough ultrasound transducers placed around the biological tissue, PAT can provide both deep penetration and high image contrast by hybrid usage of light and sound. However, considering space and measurement environmental limitations, transducers are always placed in a limited-angle way, which means that the other side without transducer coverage suffers severe information loss. With conventional image reconstruc…
