With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. Reconstructing the 3D geometric morphology of liver vessels from the contrasted CT images can enable multiple liver preoperative surgical planning applications. Automatic reconstruction of liver vessel morphology remains a challenging problem due to the morphological complexity of liver vessels and the inconsistent vessel intensities among different multi-phase contrasted CT images. On the other side, high integrity is required for the 3D reconstruction to avoid decision making biases. In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network. A fully convolutional neural network is first trained to produce the liver vessel centerline heatmap. An over-reconstructed liver vessel graph model is then traced based on the heatmap using an image processing based algorithm. We use a graph attention network to prune the false-positive branches by predicting the presence probability of each segmented branch in the initial reconstruction using the aggregated CNN features. We evaluated the proposed framework on an in-house dataset consisting of 418 multi-phase abdomen CT images with contrast. The proposed graph network pruning improves the overall reconstruction F1 score by 6.4% over the baseline. It also outperformed the other state-of-the-art curvilinear structure reconstruction algorithms.
VEELA: A Clinically-Constrained Benchmark for Liver Vessel Segmentation in Computed Tomography Angiography
Department of Radiology, Dokuz Eylul University（多尔朱·伊勒大学放射科）；Department of Computer Engineering, Istanbul Technical University（伊斯坦布尔技术大学计算机工程系）；Institute of Natural and Applied Sciences, Dokuz Eylul University（多尔朱·伊勒大学自然科学与应用科学学院）；Department of Electrical and Electronics Engineering, Dokuz Eylul University（多尔朱·伊勒大学电气与电子工程系）；Department of Radiology, University of Wisconsin-Madison（威斯康星大学麦迪逊分校放射科）；School of Medicine, Sivas Cumhuriyet University（萨瓦斯·库尔德大学医学院）；School of Medicine, Acibadem Mehmet Ali Aydinlar University（阿克塞姆·梅赫梅特·阿里·阿迪姆大学医学院）；Department of Artificial Intelligence Engineering, Bahçeşehir University（巴切希尔大学人工智能工程系）；Faculty of Pharmacy, Sivas Cumhuriyet University（萨瓦斯·库尔德大学药学院）
Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Integrate Framework: Application to Liver Tumor and Vessel 3D Reconstruction under embedded microprocessor
China University of Petroleum（中国石油大学）；Qingdao Municipal Hospital（青岛市市立医院）；University of Chinese Academy of Sciences（中国科学院大学）
Inria（法国国家信息与自动化研究所）；University of Strasbourg（斯特拉斯堡大学）；Technical University of Munich（慕尼黑技术大学）；University of Basel（巴塞尔大学）；Institute of Image-Guided Surgery（图像引导手术研究所）
Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation
LaTIM UMR 1101（拉蒂姆UMR 1101机构）；Inserm（法国国家健康与医学研究院）；University of Western Brittany（西布列塔尼大学）；University Hospital of Brest（布雷斯特大学医院）；University Hospital of Poitiers（普瓦捷大学医院）；ICube UMR 7357（ICube UMR 7357机构）；CNRS（法国国家科学研究中心）；IMT Atlantique（大西洋电信学院）
Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models
George Mason University（乔治梅森大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Beijing Jishuitan Hospital（北京积水潭医院）
BreastSegNet: Multi-label Segmentation of Breast MRI
Department of Biostatistics \& Bioinformatics, Duke University Department of Electrical；Computer Engineering, Duke University Department of Radiology, Duke University Medical Center Department of Computer Science, Duke University
