Towards realistic organ models for 3D printing and visualization

Autor: Kraft Valentin, Schumann Christian, Salzmann Daniela, Nopper Hans, Lück Thomas, Weyhe Dirk, Schenk Andrea

Publikováno v: Current Directions in Biomedical Engineering, Vol 7, Iss 1, Pp 166-170 (2021)

Three-dimensional visualizations and 3D-printed organs are used increasingly for teaching, surgery planning, patient education, and interventions. Hence, pipelines for the creation of the necessary geometric data from CT or MR images on a per-patient

Externí odkaz: https://doaj.org/article/ba63bc33506542b8a654d0431fc9a3e9

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI

Autor: Meyer, Anneke, Chlebus, Grzegorz, Rak, Marko, Schindele, Daniel, Schostak, Martin, van Ginneken, Bram, Schenk, Andrea, Meine, Hans, Hahn, Horst K., Schreiber, Andreas, Hansen, Christian

Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despi

Externí odkaz: http://arxiv.org/abs/2009.11120

A fast and robust hepatocyte quantification algorithm including vein processing

Autor: Homeyer André, Schenk Andrea, Ivanovska Tetyana, Deng Meihong, Dahmen Uta, Dirsch Olaf, Hahn Horst K, Linsen Lars

Publikováno v: BMC Bioinformatics, Vol 11, Iss 1, p 124 (2010)

Abstract Background Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after parti

Externí odkaz: https://doaj.org/article/5c9623793f4e480eb01459140816ddaa

The Liver Tumor Segmentation Benchmark (LiTS)

Autor: Bilic, Patrick, Christ, Patrick, Li, Hongwei Bran, Vorontsov, Eugene, Ben-Cohen, Avi, Kaissis, Georgios, Szeskin, Adi, Jacobs, Colin, Mamani, Gabriel Efrain Humpire, Chartrand, Gabriel, Lohöfer, Fabian, Holch, Julian Walter, Sommer, Wieland, Hofmann, Felix, Hostettler, Alexandre, Lev-Cohain, Naama, Drozdzal, Michal, Amitai, Michal Marianne, Vivantik, Refael, Sosna, Jacob, Ezhov, Ivan, Sekuboyina, Anjany, Navarro, Fernando, Kofler, Florian, Paetzold, Johannes C., Shit, Suprosanna, Hu, Xiaobin, Lipková, Jana, Rempfler, Markus, Piraud, Marie, Kirschke, Jan, Wiestler, Benedikt, Zhang, Zhiheng, Hülsemeyer, Christian, Beetz, Marcel, Ettlinger, Florian, Antonelli, Michela, Bae, Woong, Bellver, Míriam, Bi, Lei, Chen, Hao, Chlebus, Grzegorz, Dam, Erik B., Dou, Qi, Fu, Chi-Wing, Georgescu, Bogdan, Giró-i-Nieto, Xavier, Gruen, Felix, Han, Xu, Heng, Pheng-Ann, Hesser, Jürgen, Moltz, Jan Hendrik, Igel, Christian, Isensee, Fabian, Jäger, Paul, Jia, Fucang, Kaluva, Krishna Chaitanya, Khened, Mahendra, Kim, Ildoo, Kim, Jae-Hun, Kim, Sungwoong, Kohl, Simon, Konopczynski, Tomasz, Kori, Avinash, Krishnamurthi, Ganapathy, Li, Fan, Li, Hongchao, Li, Junbo, Li, Xiaomeng, Lowengrub, John, Ma, Jun, Maier-Hein, Klaus, Maninis, Kevis-Kokitsi, Meine, Hans, Merhof, Dorit, Pai, Akshay, Perslev, Mathias, Petersen, Jens, Pont-Tuset, Jordi, Qi, Jin, Qi, Xiaojuan, Rippel, Oliver, Roth, Karsten, Sarasua, Ignacio, Schenk, Andrea, Shen, Zengming, Torres, Jordi, Wachinger, Christian, Wang, Chunliang, Weninger, Leon, Wu, Jianrong, Xu, Daguang, Yang, Xiaoping, Yu, Simon Chun-Ho, Yuan, Yading, Yu, Miao, Zhang, Liping, Cardoso, Jorge, Bakas, Spyridon, Braren, Rickmer, Heinemann, Volker, Pal, Christopher, Tang, An, Kadoury, Samuel, Soler, Luc, van Ginneken, Bram, Greenspan, Hayit, Joskowicz, Leo, Menze, Bjoern

Publikováno v: Medical Image Analysis (2022) Pg. 102680

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 on Medical I

Externí odkaz: http://arxiv.org/abs/1901.04056

Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT

Autor: Meine, Hans, Chlebus, Grzegorz, Ghafoorian, Mohsen, Endo, Itaru, Schenk, Andrea

Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis of convolutional neural networks have been introduced recently. Using

Externí odkaz: http://arxiv.org/abs/1810.04017