Dr. Hans Meine

  • Senior Research Scientist
  • Computer Scientist



Nitsch J, Klein J, Moltz JH, Miller D, Sure U, Kikinis R, Meine H (2019) Neural-network-based automatic segmentation of cerebral ultrasound images for improving image-guided neurosurgery. To appear: Proc. of SPIE Medical Imaging, San Diego, USA


Chlebus G, Schenk A, Moltz JH, van Ginneken B, Hahn HK, Meine H (2018) Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific Reports 8
Chlebus G, Meine H, Abolmaali N, Schenk A (2018) Automatic Liver and Tumor Segmentation in Late-Phase MRI Using Fully Convolutional Neural Networks. Proceedings of CURAC. pp 195–200
Haensch A, Dicken V, Gass T, Morgas T, Klein J, Meine H, Hahn HK (2018) Deep learning based segmentation of organs of the female pelvis in CBCT scans for adaptive radiotherapy using CT and CBCT data. International Journal of Computer Assisted Radiology and Surgery. pp 179–180
Hänsch A, Gass T, Morgas T, Haas B, Meine H, Klein J, Hahn HK (2018) Parotid gland segmentation with deep learning using clinical vs. curated training data. Radiotherapy and Oncology. 278
Hänsch A, Schwier M, Gass T, Morgas T, Haas B, Dicken V, Meine H, Klein J, Hahn HK (2018) Evaluation of deep learning methods for parotid gland segmentation from CT images. Journal of Medical Imaging 6(1):011005
Meine H, Chlebus G, Ghafoorian M, Endo I, Schenk A (2018) Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. arXiv:1810.04017
Schenk A, Chlebus G, Meine H, Thoduka S, Abolmaali N (2018) Deep learning for liver segmentation and volumetry in late phase MRI. Proceedings of European Congress on Radiology (ECR). Springer, S474


Chlebus G, Meine H, Moltz JH, Schenk A (2017) Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering
Chlebus G, Schenk A, Thoduka S, Abolmaali N, Endo I, Meine H (2017) Comparison of Model Initialization Methods for Liver Segmentation using Statistical Shape Models. International Journal of Computer Assisted Radiology and Surgery. pp 215–216
Haensch A, Harz M, Schenk A, Endo I, Jacobs C, Hahn HK, Meine H (2017) Examining the robustness of liver segmentation using deep learning and unsupervised pre-training of a feature extractor. International Journal of Computer Assisted Radiology and Surgery. Springer International Publishing, pp 23–24


Derksen A, König L, Meine H, Heldmann S (2016) A Joint Registration and Segmentation Approach for Large Bladder Deformations in Adaptive Radiotherapy. Medical Physics: Proceedings of the AAPM 58th annual meeting, Washington, DC. American Association of Physicists in Medicine, 3429
Meine H, Izadpanah K, Lange T (2016) Image Processing Pipeline for MRI-based In-vivo Cartilage Assessment under Load. In: Heinz Lemke (ed) Computer Assisted Radiology and Surgery (CARS) 2016: Proceedings of the 30th Congress and Exhibition. Springer, S51+
Wang L, Chitiboi T, Meine H, Günther M, Hahn HK (2016) Principles and methods for automatic and semi-automatic tissue segmentation in MRI data. Magn Reson Mater Phy 29(2):95–110


Ruehaak J, Derksen A, Heldmann S, Hallmann M, Meine H (2015) Accurate CT-MR image registration for deep brain stimulation: a multi-observer evaluation study. Proc. SPIE Medical Imaging: Image Processing. 941337:pp 1–7
Vicari M, Heldmann S, Meine H, Hug F, Hennig J, Iblher N (2015) Quantitative Assessments of Facial Soft-Tissue Mobility by means of Watershed Segmentation and Constrained Elastic Registration in Upright Accelerated 3D MRI. Proceedings of the 23rd Annual Meeting ISMRM. 4204


Chen L, Hallmann M, Barrios Romero D, Wang L, Astrom M, Ryzhkov M, Nijlunsing R, Meine H (2014) Combining tubular tracking and skeletonization for fully-automatic and accurate lead localization in CT images. Proc. Computer Assisted Radiology and Surgery – Neurosurgery and ENT Surgery. pp 195–196
Demedts D, Hennemuth A, Meine H, Ojdanic D (2014) Web-based interactive visualization and assessment of medical images for clinical trials. Proc. Computer Assisted Radiology and Surgery – Display and Visualization. pp S85–S86
Heckel F, Meine H, Moltz JH, Kuhnigk J-M, Heverhagen JT, Kießling A, Buerke B, Hahn HK (2014) Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT. IEEE Trans Med Imaging 33(2):462–480
Heckel F, Moltz JH, Meine H, Geisler B, Kießling A, D'Anastasi M, Pinto dos Santos D, Theruvath AJ, Hahn HK (2014) On the Evaluation of Segmentation Editing Tools. SPIE J Med Imag 1(3):034005
Rudyanto RD, Kerkstra S, van Rikxoort EM, Fetita C, Brillet PY, Lefevre C, Xue W, Zhu X, Liang J, Oksüz I, Unay D, Kadipaşaogˇlu K, Estépar RS, Ross JC, Washko GR, Prieto JC, Hoyos MH, Orkisz M, Meine H, Hüllebrand M, Stöcker C, Mir FL, Naranjo V, Villanueva E, Staring M, Xiao C, Stoel BC, Fabijanska A, Smistad E, Elster AC, Lindseth F, Foruzan AH, Kiros R, Popuri K, Cobzas D, Jimenez-Carretero D, Santos A, Ledesma-Carbayo MJ, Helmberger M, Urschler M, Pienn M, Bosboom DG, Campo A, Prokop M, de Jong PA, Ortiz-de-Solorzano C, Muñoz-Barrutia A, van Ginneken B (2014) Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med Image Anal 18(7):1217–1232