Comparison of Patch-Based Conditional Generative Adversarial Neural Net Models with Emphasis on Model Robustness for Use in Head and Neck Cases for MR-Only planning
A total of twenty paired CT and MR images were used in this study to investigate two conditional generative adversarial networks, Pix2Pix, and Cycle GAN, for generating synthetic CT images for Headand Neck cancer cases. Ten of the patient cases were used for training and included such common artifacts as dental implants; the remaining ten testing cases were used for testing and included a larger range of image features commonly found in clinical head and neck cases. These features included strong metal artifacts from dental implants, one case with a metal implant, and one case with abnormal anatomy. The original CT images were deformably registered to the mDixon FFE MR images to minimize the effects of processing the MR images. The sCT generation accuracy and robustness were evaluated using Mean Absolute Error (MAE) based on the Hounsfield Units (HU) for three regions (whole body, bone, and air within the body), Mean Error (ME) to observe systematic average offset errors in the sCT generation, and dosimetric evaluation of all clinically relevant structures. For the test set the MAE for the Pix2Pix and Cycle GAN models were 92.4 $\pm$ 13.5 HU, and 100.7 $\pm$ 14.6 HU, respectively, for the body region, 166.3 $\pm$ 31.8 HU, and 184 $\pm$ 31.9 HU, respectively, for the bone region, and 183.7 $\pm$ 41.3 HU and 185.4 $\pm$ 37.9 HU for the air regions. The ME for Pix2Pix and Cycle GAN were 21.0 $\pm$ 11.8 HU and 37.5 $\pm$ 14.9 HU, respectively. Absolute Percent Mean/Max Dose Errors were less than 2% for the PTV and all critical structures for both models, and DRRs generated from these models looked qualitatively similar to CT generated DRRs showing these methods are promising for MR-only planning.
UMIT – Private University for Health Sciences, Medical Informatics and Technology（UMIT——健康科学、医学信息学与技术私立大学）
The segmentation of organs at risk (OAR) is a required precondition for the cancer treatment with image guided radiation therapy. The automation of the segmentation task is therefore of high clinical relevance. Deep Learning (DL) based medical image segmentation is currently the most successful approach, but suffers from the over-presence of the background class and the anatomically given organ size difference, which is most severe in the head and neck (HAN) area. To tackle the HAN area specific class imbalance problem we first optimize the patch-size of the currently best performing general p…
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University Hospital Erlangen（埃尔朗根大学医院）；University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）；Johannes Gutenberg-Universität Mainz（美因茨约翰内斯·古腾堡大学）；University Medical Center Mainz（美因茨大学医学中心）
Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to …
Huazhong University of Science and Technology（华中科技大学）；Horizon Robotics；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
University of California, Irvine（加州大学欧文分校）；Lenovo Research（联想研究院）；Deepvoxel Inc（深度体素公司）；Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属上海第一人民医院）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）
Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: 1) a new encoding scheme to allow auto-segmentation on whole-volume CT images instead of local patches or subsets of slices, 2) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and 3) a new loss function combining…
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Maryland Medical Center（马里兰大学医学中心）；Central Arkansas Radiation Therapy Institute（中央阿肯色放射治疗研究所）
The Johns Hopkins University Applied Physics Laboratory（约翰霍普金斯大学应用物理实验室）
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physically realizable, yet most work has focused on 2D domain with recent forays into 3D. Characterizing the robustness properties of patch attacks and their invariance to 3D pose is important, yet not fully elucidated, and is the focus of this paper. To this end, several contributions are made here: A) we develop a new metric called mean Attack Success over Transformations (mAST) to evaluate patch attack robustness and inva…
PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport
School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；School of Electronic Information, Qingdao University（青岛大学电子信息学院）；School of Engineering and Sustainable Development, De Montfort University（德蒙福特大学工程与可持续发展学院）；Department of Computing and Decision Science, Lingnan University（岭南大学计算与决策科学系）
University Medical Center Utrecht（乌得勒支大学医学中心）；Radboud University Medical Center Nijmegen（奈梅亨拉德堡德大学医学中心）；Utrecht University（乌得勒支大学）
Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. We propose an iterative instance segmentation approach that uses a fully convolutional neural network to segment and label vertebrae one after the other, …
Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss
Univ. Bordeaux, CNRS, LaBRI, UMR 5800, F-33400 Talence, France（波尔多大学，法国国家科学研究中心，LaBRI研究院，UMR 5800）；Universidad Autónoma de Madrid, Madrid, 28049, Spain（马德里自治大学，西班牙）；Pazmany Peter Catholic University, Budapest, 1083, Hungary（佩斯大学，匈牙利）；RIKEN Center for Integrative Medical Sciences, Medical Data Deep Learning Team, Tokyo, Japan（日本再生医学科学中心，医学数据深度学习团队）
