@article{SekuboyinaHusseiniBayatetal., author = {Sekuboyina, Anjany and Husseini, Malek E. and Bayat, Amirhossein and L{\"o}ffler, Maximilian and Liebl, Hans and Li, Hongwei and Tetteh, Giles and Kukačka, Jan and Payer, Christian and Štern, Darko and Urschler, Martin and Chen, Maodong and Cheng, Dalong and Lessmann, Nikolas and Hu, Yujin and Wang, Tianfu and Yang, Dong and Xu, Daguang and Ambellan, Felix and Amiranashvili, Tamaz and Ehlke, Moritz and Lamecker, Hans and Lehnert, Sebastian and Lirio, Marilia and de Olaguer, Nicol{\´a}s P{\´e}rez and Ramm, Heiko and Sahu, Manish and Tack, Alexander and Zachow, Stefan and Jiang, Tao and Ma, Xinjun and Angerman, Christoph and Wang, Xin and Brown, Kevin and Kirszenberg, Alexandre and Puybareau, {\´E}lodie and Chen, Di and Bai, Yiwei and Rapazzo, Brandon H. and Yeah, Timyoas and Zhang, Amber and Xu, Shangliang and Hou, Feng and He, Zhiqiang and Zeng, Chan and Xiangshang, Zheng and Liming, Xu and Netherton, Tucker J. and Mumme, Raymond P. and Court, Laurence E. and Huang, Zixun and He, Chenhang and Wang, Li-Wen and Ling, Sai Ho and Huynh, L{\^e} Duy and Boutry, Nicolas and Jakubicek, Roman and Chmelik, Jiri and Mulay, Supriti and Sivaprakasam, Mohanasankar and Paetzold, Johannes C. and Shit, Suprosanna and Ezhov, Ivan and Wiestler, Benedikt and Glocker, Ben and Valentinitsch, Alexander and Rempfler, Markus and Menze, Bj{\"o}rn H. and Kirschke, Jan S.}, title = {VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images}, series = {Medical Image Analysis}, volume = {73}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2021.102166}, abstract = {Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.}, language = {en} } @article{RohrHerrmannIlmetal., author = {Rohr, Ulrich-Peter and Herrmann, Pia and Ilm, Katharina and Zhang, Hai and Lohmann, Sabine and Reiser, Astrid and Muranyi, Andrea and Smith, Janice and Burock, Susen and Osterland, Marc and Leith, Katherine and Singh, Shalini and Brunhoeber, Patrick and Bowermaster, Rebecca and Tie, Jeanne and Christie, Michael and Wong, Hui-Li and Waring, Paul and Shanmugam, Kandavel and Gibbs, Peter and Stein, Ulrike}, title = {Prognostic value of MACC1 and proficient mismatch repair status for recurrence risk prediction in stage II colon cancer patients: the BIOGRID studies}, series = {Annals of Oncology}, volume = {28}, journal = {Annals of Oncology}, number = {8}, doi = {10.1093/annonc/mdx207}, pages = {1869 -- 1875}, abstract = {Background We assessed the novel MACC1 gene to further stratify stage II colon cancer patients with proficient mismatch repair (pMMR). Patients and methods Four cohorts with 596 patients were analyzed: Charit{\´e} 1 discovery cohort was assayed for MACC1 mRNA expression and MMR in cryo-preserved tumors. Charit{\´e} 2 comparison cohort was used to translate MACC1 qRT-PCR analyses to FFPE samples. In the BIOGRID 1 training cohort MACC1 mRNA levels were related to MACC1 protein levels from immunohistochemistry in FFPE sections; also analyzed for MMR. Chemotherapy-na{\"i}ve pMMR patients were stratified by MACC1 mRNA and protein expression to establish risk groups based on recurrence-free survival (RFS). Risk stratification from BIOGRID 1 was confirmed in the BIOGRID 2 validation cohort. Pooled BIOGRID datasets produced a best effect-size estimate. Results In BIOGRID 1, using qRT-PCR and immunohistochemistry for MACC1 detection, pMMR/MACC1-low patients had a lower recurrence probability versus pMMR/MACC1-high patients (5-year RFS of 92\% and 67\% versus 100\% and 68\%, respectively). In BIOGRID 2, longer RFS was confirmed for pMMR/MACC1-low versus pMMR/MACC1-high patients (5-year RFS of 100\% versus 90\%, respectively). In the pooled dataset, 6.5\% of patients were pMMR/MACC1-low with no disease recurrence, resulting in a 17\% higher 5-year RFS (95\% CI (12.6-21.3\%)) versus pMMR/MACC1-high patients (P=0.037). Outcomes were similar for pMMR/MACC1-low and deficient MMR (dMMR) patients (5-year RFS of 100\% and 96\%, respectively). Conclusions MACC1 expression stratifies colon cancer patients with unfavorable pMMR status. Stage II colon cancer patients with pMMR/MACC1-low tumors have a similar favorable prognosis to those with dMMR with potential implications for the role of adjuvant therapy.}, language = {en} } @misc{RohrHerrmannIlmetal., author = {Rohr, Ulrich-Peter and Herrmann, Pia and Ilm, Katharina and Zhang, Hai and Lohmann, Sabine and Reiser, Astrid and Muranyi, Andrea and Smith, Janice and Burock, Susen and Osterland, Marc and Leith, Katherine and Singh, Shalini and Brunhoeber, Patrick and Bowermaster, Rebecca and Tie, Jeanne and Christie, Michael and Wong, Hui-Li and Waring, Paul and Shanmugam, Kandavel and Gibbs, Peter and Stein, Ulrike}, title = {Prognostic value of MACC1 and proficient mismatch repair status for recurrence risk prediction in stage II colon cancer patients: the BIOGRID studies}, issn = {1438-0064}, doi = {10.1093/annonc/mdx207}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-64184}, abstract = {Background We assessed the novel MACC1 gene to further stratify stage II colon cancer patients with proficient mismatch repair (pMMR). Patients and methods Four cohorts with 596 patients were analyzed: Charit{\´e} 1 discovery cohort was assayed for MACC1 mRNA expression and MMR in cryo-preserved tumors. Charit{\´e} 2 comparison cohort was used to translate MACC1 qRT- PCR analyses to FFPE samples. In the BIOGRID 1 training cohort MACC1 mRNA levels were related to MACC1 protein levels from immunohistochemistry in FFPE sections; also analyzed for MMR. Chemotherapy-na{\"i}ve pMMR patients were stratified by MACC1 mRNA and protein expression to establish risk groups based on recurrence-free survival (RFS). Risk stratification from BIOGRID 1 was confirmed in the BIOGRID 2 validation cohort. Pooled BIOGRID datasets produced a best effect-size estimate. Results In BIOGRID 1, using qRT-PCR and immunohistochemistry for MACC1 detection, pMMR/MACC1-low patients had a lower recurrence probability versus pMMR/MACC1-high patients (5-year RFS of 92\% and 67\% versus 100\% and 68\%, respectively). In BIOGRID 2, longer RFS was confirmed for pMMR/MACC1-low versus pMMR/MACC1-high patients (5-year RFS of 100\% versus 90\%, respectively). In the pooled dataset, 6.5\% of patients were pMMR/MACC1-low with no disease recurrence, resulting in a 17\% higher 5-year RFS (95\% CI (12.6-21.3\%)) versus pMMR/MACC1-high patients (P=0.037). Outcomes were similar for pMMR/MACC1-low and deficient MMR (dMMR) patients (5-year RFS of 100\% and 96\%, respectively). Conclusions MACC1 expression stratifies colon cancer patients with unfavorable pMMR status. Stage II colon cancer patients with pMMR/MACC1-low tumors have a similar favorable prognosis to those with dMMR with potential implications for the role of adjuvant therapy.}, language = {en} } @article{XiaoZhangAndrzejaketal.2004, author = {Xiao, Li and Zhang, Xiaodong and Andrzejak, Artur and Chen, Songqing}, title = {Building a Large and Efficient Hybrid Peer-to-Peer Internet Caching System}, series = {IEEE Trans. Knowl. Data Eng.}, volume = {16}, journal = {IEEE Trans. Knowl. Data Eng.}, number = {6}, doi = {10.1109/TKDE.2004.1}, pages = {754 -- 769}, year = {2004}, language = {en} } @inproceedings{VosTielbeekNazirogluetal.2012, author = {Vos, Franciscus and Tielbeek, Jeroen and Naziroglu, Robiel and Li, Zhang and Schueffler, Peter and Mahapatra, Dwarikanath and Wiebel, Alexander and Lavini, Christina and Buhmann, Joachim and Hege, Hans-Christian and Stoker, Jaap and van Vliet, Lucas}, title = {Computational modeling for assessment of IBD: to be or not to be?}, series = {2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, booktitle = {2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)}, doi = {10.1109/EMBC.2012.6346837}, pages = {3974 -- 3977}, year = {2012}, language = {en} } @article{ZhangLiSchuette2021, author = {Zhang, Wei and Li, Tiejun and Sch{\"u}tte, Christof}, title = {Solving eigenvalue PDEs of metastable diffusion processes using artificial neural networks}, series = {Journal of Computational Physics}, volume = {465}, journal = {Journal of Computational Physics}, doi = {10.1016/j.jcp.2022.111377}, year = {2021}, abstract = {In this paper, we consider the eigenvalue PDE problem of the infinitesimal generators of metastable diffusion processes. We propose a numerical algorithm based on training artificial neural networks for solving the leading eigenvalues and eigenfunctions of such high-dimensional eigenvalue problem. The algorithm is useful in understanding the dynamical behaviors of metastable processes on large timescales. We demonstrate the capability of our algorithm on a high-dimensional model problem, and on the simple molecular system alanine dipeptide.}, language = {en} } @article{ZhaoZhangLi, author = {Zhao, Yue and Zhang, Wei and Li, Tiejun}, title = {EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation}, series = {National Science Review}, journal = {National Science Review}, doi = {10.1093/nsr/nwae052}, abstract = {We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product space. Remarkably, our loss function has an intimate connection with the steady entropy production rate (EPR), enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness, and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an 8D limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.}, language = {en} }