Patrick Ferdinand Christ
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View article: <i>MedShapeNet</i> – a large-scale dataset of 3D medical shapes for computer vision
<i>MedShapeNet</i> – a large-scale dataset of 3D medical shapes for computer vision Open
Objectives The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. …
View article: The Liver Tumor Segmentation Benchmark (LiTS)
The Liver Tumor Segmentation Benchmark (LiTS) Open
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences o…
View article: The Medical Segmentation Decathlon
The Medical Segmentation Decathlon Open
View article: The Medical Segmentation Decathlon
The Medical Segmentation Decathlon Open
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various se…
View article: A large annotated medical image dataset for the development and evaluation of segmentation algorithms
A large annotated medical image dataset for the development and evaluation of segmentation algorithms Open
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data …
View article: The Liver Tumor Segmentation Benchmark (LiTS)
The Liver Tumor Segmentation Benchmark (LiTS) Open
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences o…
View article: Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on <sup>68</sup>Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods
Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on <sup>68</sup>Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods Open
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). 68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body det…
View article: Convolutional Neural Networks for Classification and Segmentation of Medical Images
Convolutional Neural Networks for Classification and Segmentation of Medical Images Open
Automatic Detection, segmentation and classification of cancer plays an important role in the development of computer-aided diagnosis systems (CADs). This thesis investigates the application of convolutional and fully convolutional neural …
View article: Diabetes60 — Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks
Diabetes60 — Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks Open
In this paper we propose a challenging new computer vision task of inferring Bread Units (BUs) from food images. Assessing nutritional information and nutrient volume from a meal is an important task for diabetes patients. At the moment, d…
View article: Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation Open
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer. However, design of accurate automated methods remains challenging due to high noise in CT scans, low contrast between liver a…
View article: SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks
SurvivalNet: Predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3D convolutional neural networks Open
Automatic non-invasive assessment of hepatocellular carcinoma (HCC) malignancy has the potential to substantially enhance tumor treatment strategies for HCC patients. In this work we present a novel framework to automatically characterize …
View article: Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks Open
Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automaticall…
View article: Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields Open