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Behdad Dasht Bozorg (B. Dashtbozorg)

Behdad Dasht Bozorg

Senior Scientist

Phone Email Theo Ruers

Behdad Dashtbozorg is a senior scientist at the Netherlands Cancer Institute. After graduating in Electrical Engineering in 2010, he started his journey in the field of Medical Image Analysis and Artificial Intelligence at the INEB - Instituto de Engenharia Biomédica, University of Porto in Portugal, receiving his PhD in 2015. Subsequently, he joined Eindhoven University of Technology as a postdoctoral researcher, focusing on the development and implementation of advanced machine/deep learning algorithms for computer-aided diagnostics. In 2018, he furthered his career at the Netherlands Cancer Institute (NKI) as a senior scientific researcher in the image-guided surgery group, running numerous projects in smart surgical tool development, tissue sensing, and applying machine learning and computer vision to enhance image-guided surgery applications and early diagnostics. As the principal investigator of several studies funded by the Dutch Cancer Society (KWF) and other agencies, his research focuses on leveraging AI for medical data/image analysis and multimodality imaging to advance surgery and early cancer detection.

Fields of expertise

AI in Image Guided Surgery

AI in early Diagnostics

Medical Image & Data Analysis

Work experience

2020-2024
Senior postdoc, Image-Guided Surgery, NKI, Amsterdam

2018-2020
Postdoc researcher, Image-Guided Surgery, NKI, Amsterdam

Our group

Image-Guided Surgery

Personal website

LinkedIn Behdad Dasht Bozorg

Available Student Projects

Drsprobe
Using diffuse reflectance spectroscopy for tumor margin detection during breast conserving surgery
Drsusprobe
Smart surgical instruments
Hyperspectral Deep Learning
Tumor detection using deep learning and Hyperspectral imaging
Spectraskin
SpectraSkin-Skin Cancer Detection with Mobile Hyperspectral Imaging
3DUS
Trackerless 3D Point-Of-Care Ultrasound Reconstruction
HQUS
Enhancing Point-of-Care Ultrasound Image Quality Using a Conditional GAN
XAI
XAI for real-time ultrasound imaging in oncology
POCUS Breast
AI-powered POCUS for Breast Tumor Detection
POCUS Prostate
AI-Powered POCUS for Early Prostate Cancer Detection

List of Publications

  • Boellaard, Thierry N., Roy van Erck, Sophia H. van der Graaf, Lisanne de Boer, Henk G. van der Poel, Laura S. Mertens, Pim J. van Leeuwen, and Behdad Dashtbozorg. "Comparing AI and Manual Segmentation of Prostate MRI: Towards AI-Driven 3D-Model-Guided Prostatectomy." Diagnostics 15, no. 9 (2025): 1141.
  • Jong, Lynn-Jade S., Dinusha Veluponnar, Freija Geldof, Joyce Sanders, Marcos Da Silva Guimaraes, Marie-Jeanne TFD Vrancken Peeters, Frederieke van Duijnhoven, Henricus JCM Sterenborg, Behdad Dashtbozorg, and Theo JM Ruers. "Toward real-time margin assessment in breast-conserving surgery with hyperspectral imaging." Scientific reports 15, no. 1 (2025): 9556.
  • Natali, T., Kurucz, L.M., Fusaglia, M., Mertens, L.S., Ruers, T.J.M., van Leeuwen, P.J., Dashtbozorg, B., 2025. Automatic Prostate Volume Estimation in Transabdominal Ultrasound Images. arXiv Prepr. arXiv2502.07859.
  • Feenstra, L., van der Stel, S.D., Da Silva Guimaraes, M., Dashtbozorg, B., Ruers, T.J.M., 2024. Point projection mapping system for tracking, registering, labeling, and validating optical tissue measurements. J. imaging 10, 37.
  • Feenstra, L., Lambregts, M., Ruers, T.J.M., Dashtbozorg, B., 2024. Deformable multi-modal image registration for the correlation between optical measurements and histology images. J. Biomed. Opt. 29, 66007.
  • Geldof, F., Schrage, Y.M., van Houdt, W.J., Sterenborg, H.J.C.M., Dashtbozorg, B., Ruers, T.J.M., 2024. Toward the use of diffuse reflection spectroscopy for intra-operative tissue discrimination during sarcoma surgery. J. Biomed. Opt. 29, 27001.
  • Jong, L.-J.S., Appelman, J.G.C., Sterenborg, H.J.C.M., Ruers, T.J.M., Dashtbozorg, B., 2024. Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images. Sensors 24, 1567.
  • Geldof, F., 2024. Advancements in margin assessment for oncological surgery: Integrating diffuse reflectance spectroscopy and ultrasound imaging.
  • de Roode, L.M., de Boer, L., Guimares, M.D.S., Van Leeuwen, P., Van Der Poel, H., Dashtbozorg, B., Ruers, T., 2024. Diffuse reflectance spectroscopy during prostatectomy; towards intraoperative margin assessment. Eur. Urol. 85, S2036–S2037.
  • Veluponnar, D., de Boer, L.L., Dashtbozorg, B., Jong, L.-J.S., Geldof, F., Guimaraes, M.D.S., Sterenborg, H.J.C.M., Vrancken-Peeters, M.-J.T.F.D., van Duijnhoven, F., Ruers, T., 2024. Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy. J. Biomed. Opt. 29, 45006.
  • Natali, T., Wijkhuizen, M., Kurucz, L., Fusaglia, M., van Leeuwen, P.J., Ruers, T.J.M., Dashtbozorg, B., 2024. Automatic real-time prostate detection in transabdominal ultrasound images, in: Medical Imaging with Deep Learning.
  • Wijkhuizen, M., van Karnenbeek, L., Geldof, F., Ruers, T.J.M., Dashtbozorg, B., 2024. Ultrasound tumor detection using an adapted Mask-RCNN with a continuous objectness score, in: Medical Imaging with Deep Learning.
  • Veluponnar, D., Dashtbozorg, B., Guimaraes, M.D.S., Peeters, M.-J.T.F.D.V., Boer, L.L. de, Ruers, T.J.M., 2024. Resection Ratios and Tumor Eccentricity in Breast-Conserving Surgery Specimens for Surgical Accuracy Assessment. Cancers (Basel). 16, 1813.
  • Zhang, D., Lu, C., Tan, T., Dashtbozorg, B., Long, X., Xu, X., Zhang, J., Shan, C., 2024. BSANet: Boundary-aware and scale-aggregation networks for CMR image segmentation. Neurocomputing 599, 128125.
  • Wyatt, L.S., van Karnenbeek, L.M., Wijkhuizen, M., Geldof, F., Dashtbozorg, B., 2024. Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review. Appl. Sci. 14, 8108.
  • van der Pol, H.G.A., van Karnenbeek, L.M., Wijkhuizen, M., Geldof, F., Dashtbozorg, B., 2024. Deep learning for point-of-care ultrasound image quality enhancement: a review. Appl. Sci. 14, 7132.
  • de Roode, L.M., de Boer, L.L., Guimaraes, M.D.S., van Leeuwen, P.J., van der Poel, H.G., Dashtbozorg, B., Ruers, T.J.M., 2024. Feasibility of Diffuse Reflection Spectroscopy for Intraoperative Margin Assessment During Prostatectomy. Eur. Urol. Open Sci. 67, 62–68.
  • Schoop, R.A.L., de Roode, L.M., de Boer, L.L., Dashtbozorg, B., 2024. Framework for deep learning based Multi-Modality image registration of snapshot and pathology images. IEEE J. Biomed. Heal. Informatics.
  • Jong, L.-J.S., Post, A.L., Geldof, F., Dashtbozorg, B., Ruers, T.J.M., Sterenborg, H.J.C.M., 2024. Separating Surface Reflectance from Volume Reflectance in Medical Hyperspectral Imaging. Diagnostics 14, 1812.
  • Adriaans, C.A., Wijkhuizen, M., van Karnenbeek, L.M., Geldof, F., Dashtbozorg, B., 2024. Trackerless 3D Freehand Ultrasound Reconstruction: A Review. Appl. Sci. 14, 7991.
  • Kurucz, L., Natali, T., Fusaglia, M., Dashtbozorg, B., 2024. Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging.
  • Veluponnar, D., Boer, L.L. De, Geldof, F., Jong, L.S., Da, M., Guimaraes, S., Peeters, M.T.F.D.V., Duijnhoven, F. Van, Ruers, T., Dashtbozorg, B., 2023. Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images. Cancers (Basel). 15, 1652.
  • Hoogteijling, N., Veluponnar, D., de Boer, L., Dashtbozorg, B., Peeters, M.-J.V., van Duijnhoven, F., Ruers, T., 2023. Toward automatic surgical margin assessment using ultrasound imaging during breast cancer surgery. Eur. J. Surg. Oncol. 49, e108–e109.
  • J. Friemann  M. Fagerström, S. M. Mirkhalaf, B.D., 2023. A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites. Int. J. Numer. Methods Eng. 1–23.
  • Jong, L.-J., de Kruif, N., Geldof, F., Veluponnar, D., Sanders, J., Peeters, M.-J.V., van Duijnhoven, F., Sterenborg, H., Dashtbozorg, B., Ruers, T., 2023. Resection margin assessment in breast lumpectomy specimens using deep learning-based hyperspectral imaging, in: Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XXI. SPIE, p. PC123680F.
  • Geldof, F., Witteveen, M., Sterenborg, H., Dashtbozorg, B., Ruers, T., 2023. Tissue classification during colorectal cancer surgery using diffuse reflectance spectroscopy at the fingertip: design and performance of a compact side-firing probe, in: Optical Tomography and Spectroscopy of Tissue XV. SPIE, p. PC123760M.
  • Jong, L.-J.S., Post, A.L., Veluponnar, D., Geldof, F., Sterenborg, H.J.C.M., Ruers, T.J.M., Dashtbozorg, B., 2023. Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers (Basel). 15, 2679.
  • Soleimani, M., Dashtbozorg, B., Mirkhalaf, M., Mirkhalaf, S.M., 2023. A multiphysics-based artificial neural networks model for atherosclerosis. Heliyon 9.
  • Veluponnar, D., Dashtbozorg, B., Jong, L.-J.S., Geldof, F., Da Silva Guimaraes, M., Vrancken Peeters, M.-J.T.F.D., Van Duijnhoven, F., Sterenborg, H.J.C.M., Ruers, T.J.M., De Boer, L.L., 2023. Diffuse reflectance spectroscopy for accurate margin assessment in breast-conserving surgeries: importance of an optimal number of fibers. Biomed. Opt. Express 14, 4017–4036.
  • Geldof, F., Pruijssers, C.W.A., Jong, L.-J.S., Veluponnar, D., Ruers, T.J.M., Dashtbozorg, B., 2023. Tumor segmentation in colorectal ultrasound images using an ensemble transfer learning model: Towards intra-operative margin assessment. Diagnostics 13, 3595.
  • Geldof, F., Dashtbozorg, B., Hendriks, B.H.W., Sterenborg, H.J.C.M., Ruers, T.J.M., 2022. Layer thickness prediction and tissue classification in two-layered tissue structures using diffuse reflectance spectroscopy. Sci. Rep. 12, 1–12. https://doi.org/10.1038/s41598-022-05751-5
  • Jong, L.-J.S., de Kruif, N., Geldof, F., Veluponnar, D., Sanders, J., Vrancken Peeters, M.-J.T.F.D., van Duijnhoven, F., Sterenborg, H.J.C.M., Dashtbozorg, B., Ruers, T.J.M., 2022. Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging. Biomed. Opt. Express 13, 2581–2604.
  • Geldof, F., van Duren, K., Jong, L.-J., Veluponnar, D., Sterenborg, H., Dashtbozorg, B., Ruers, T., 2022. Classification of multilayered cancerous colorectal tissue using fiber-array diffuse reflectance spectroscopy and a multi-output neural network, in: Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX. SPIE, p. PC119490C.
  • Geldof, F., Pruijssers, S., Jong, L.-J.S., Veluponnar, D., Ruers, T., Dashtbozorg, B., 2022. Toward complete colorectal tumor resection using intraoperative ultrasound and ensemble learning, in: Medical Imaging with Deep Learning.
  • Geldof, F., Witteveen, M., Sterenborg, H.J.C.M., Ruers, T.J.M., Dashtbozorg, B., 2022. Diffuse reflection spectroscopy at the fingertip: design and performance of a compact side-firing probe for tissue discrimination during colorectal cancer surgery. Biomed. Opt. Express 14, 128–147.
  • Geldof, F., Jong, L.-J., Dashtbozorg, B., Hendriks, B.H.W., Sterenborg, H.J.C.M., Ruers, T.J.M., 2021. Combining diffuse reflectance spectroscopy and ultrasound imaging for resection margin assessment during colorectal cancer surgery, in: Multimodal Biomedical Imaging XVI. SPIE, pp. 8–13.
  • Mentges, N., Dashtbozorg, B., Mirkhalaf, S.M., 2021. A micromechanics-based artificial neural networks model for elastic properties of short fiber composites. Compos. Part B Eng. 213, 108736.
  • Friemann, J., 2021. Predicting the elasto-plastic response of short fiber composites using deep neutral networks trained on micro-mechanical simulations.
  • Kho, E., Dashtbozorg, B., Sanders, J., Vrancken Peeters, M.-J.T.F.D., van Duijnhoven, F., Sterenborg, H.J.C.M., Ruers, T.J.M., 2021. Feasibility of ex vivo margin assessment with hyperspectral imaging during breast-conserving surgery: From imaging tissue slices to imaging lumpectomy specimen. Appl. Sci. 11, 8881.
  • Saaedi, M., 2021. Predicting the Cancer Tumor Position in Liver Using Finite Element Analysis (FEA) and Artificial Intelligence (AI).
  • Huang, F., Tan, T., Dashtbozorg, B., Zhou, Y., Romeny, B.M.T.H., 2020. From Local to Global: A Graph Framework for Retinal Artery/Vein Classification. IEEE Trans. Nanobioscience 19. https://doi.org/10.1109/TNB.2020.3004481
  • Li, W., Schram, M., Berendschot, T., Webers, C., Kroon, A., van der Kallen, C., Henry, R., Koster, A., Dagnelie, P., Schaper, N., 2020. WITHDRAWN: 4.7 Prediabetes and Type 2 Diabetes are Associated With Wider Retina Arterioles and Venules. Artery Res.
  • Zhang, D., Huang, F., Khansari, M., Berendschot, T.T.J.M., Xu, X., Dashtbozorg, B., Sun, Y., Zhang, J., Tan, T., 2020. Automatic corneal nerve fiber segmentation and geometric biomarker quantification. Eur. Phys. J. Plus 135, 266.
  • Li, W., Schram, M.T., Berendschot, T.T.J.M., Webers, C.A.B., Kroon, A.A., van der Kallen, C.J.H., Henry, R.M.A., Schaper, N.C., Huang, F., Dashtbozorg, B., 2020. Type 2 diabetes and HbA 1c are independently associated with wider retinal arterioles: the Maastricht study. Diabetologia 63, 1408–1417.
  • Beletkaia, E., Dashtbozorg, B., Jansen, R.G., Ruers, T.J.M., Offerhaus, H.L., 2020. Nonlinear multispectral imaging for tumor delineation. J. Biomed. Opt. 25, 96001.
  • Baltussen, E.J.M., Sterenborg, H.J.C.M., Ruers, T.J.M., Dashtbozorg, B., 2019. Optimizing algorithm development for tissue classification in colorectal cancer based on diffuse reflectance spectra. Biomed. Opt. Express 10, 6096. https://doi.org/10.1364/boe.10.006096
  • Zhang, J., Dashtbozorg, B., Huang, F., Tan, T., ter Haar Romeny, B.M., 2019. A fully automated pipeline of extracting biomarkers to quantify vascular changes in retina-related diseases. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 7. https://doi.org/10.1080/21681163.2018.1519851
  • Heslinga, F.G., Pluim, J.P.W., Dashtbozorg, B., Berendschot, T.T.J.M., Houben, A.J.H.M., Henry, R.M.A., Veta, M., 2019. Approximation of a pipeline of unsupervised retina image analysis methods with a CNN, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2512393
  • Baltussen, E.J.M., de Koning, S.G.B., Dashtbozorg, B., Sanders, J., Aalbers, A.G.J., Kok, N.F.M., Beets, G.L., Kuhlmann, K.F.D., Hendriks, B.H.W., Sterenborg, H.J.C.M., 2019. The use of diffuse reflectance spectroscopy to distinguish tumor from fibrosis in rectal cancer patients who received neoadjuvant radiotherapy (Conference Presentation), in: Optical Tomography and Spectroscopy of Tissue XIII. SPIE, p. 108740K.
  • Heslinga, F., Pluim, J., Bozorg, B.D., Berendschot, T., Houben, A.J.H.M., Veta, M., 2019. Retinal microvascular biomarker extraction on fundus images from the Maastricht study using supervised deep learning. J. Vasc. Res. 56, 120.
  • Li, W., Schram, M.T., Berendschot, T., Webers, C., Kroon, A.A., van der Kallen, C., Henry, R., Koster, A., Dagnelie, P., Schaper, N., 2019. Postocclusive Reactive Hyperemia of the Cutaneous Microcirculation: Impact of Different Mechanisms. J. Vasc. Res. 56, 73.
  • Huang, F., Abbasi-Sureshjani, S., Zhang, J., Bekkers, E.J., Dashtbozorg, B., ter Haar Romeny, B.M., 2019. Vascular biomarkers for diabetes and diabetic retinopathy screening, in: Computational Retinal Image Analysis. Academic Press, pp. 319–352.
  • Trucco, E., MacGillivray, T., Xu, Y., 2019. Computational retinal image analysis: tools, applications and perspectives.
  • Kho, E., Dashtbozorg, B., de Boer, L.L., Van de Vijver, K.K., Sterenborg, H.J.C.M., Ruers, T.J.M., 2019. Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information. Biomed. Opt. Express 10, 4496. https://doi.org/10.1364/boe.10.004496
  • Zhang, J., Dashtbozorg, B., Huang, F., Berendschot, T.T.J.M., ter Haar Romeny, B.M., 2018. Analysis of retinal vascular biomarkers for early detection of diabetes, Lecture Notes in Computational Vision and Biomechanics. https://doi.org/10.1007/978-3-319-68195-5_88
  • Li, Z., Huang, F., Zhang, J., Dashtbozorg, B., Abbasi-Sureshjani, S., Sun, Y., Long, X., Yu, Q., Romeny, B.T.H., Tan, T., 2018. Multi-modal and multi-vendor retina image registration. Biomed. Opt. Express 9. https://doi.org/10.1364/BOE.9.000410
  • Zhang, J., Bekkers, E., Chen, D., Berendschot, T.T.J.M., Schouten, J., Pluim, J.P.W., Shi, Y., Dashtbozorg, B., Romeny, B.M.T.H., 2018. Reconnection of Interrupted Curvilinear Structures via Cortically Inspired Completion for Ophthalmologic Images. IEEE Trans. Biomed. Eng. 65. https://doi.org/10.1109/TBME.2017.2787025
  • Dashtbozorg, B., Zhang, J., Huang, F., Romeny, B.M.T.H., 2018. Retinal Microaneurysms Detection using Local Convergence Index Features. IEEE Trans. Image Process. https://doi.org/10.1109/TIP.2018.2815345
  • Huang, F., Dashtbozorg, B., Zhang, J., Yeung, A., Berendschot, T.T.J.M., ter Haar Romeny, B.M., 2018. Validation study on retinal vessel caliber measurement technique, Lecture Notes in Computational Vision and Biomechanics. https://doi.org/10.1007/978-3-319-68195-5_89
  • Abbasi-Sureshjani, S., Dashtbozorg, B., ter Haar Romeny, B.M., Fleuret, F., 2018. Exploratory study on direct prediction of diabetes using deep residual networks, Lecture Notes in Computational Vision and Biomechanics. https://doi.org/10.1007/978-3-319-68195-5_86
  • Huang, F., Dashtbozorg, B., Tan, T., ter Haar Romeny, B.M., 2018. Retinal artery/vein classification using genetic-search feature selection. Comput. Methods Programs Biomed. 161. https://doi.org/10.1016/j.cmpb.2018.04.016
  • Huang, F., Dashtbozorg, B., Romeny, B.M. ter H., 2018. Artery/vein classification using reflection features in retina fundus images. Mach. Vis. Appl. 29, 23–34.
  • Heslinga, F.G., Pluim, J.P.W., Dashtbozorg, B., Berendschot, T.T.J.M., Houben, A., Veta, M., 2018. Distilling a Pipeline of Retinal Image Analysis Tools Into a Single CNN.
  • Brouwer de Koning, S.G., Baltussen, E.J.M., Karakullukcu, M.B., Dashtbozorg, B., Smit, L.A., Dirven, R., Hendriks, B.H.W., Sterenborg, H.J.C.M., Ruers, T.J.M., 2018. Toward complete oral cavity cancer resection using a handheld diffuse reflectance spectroscopy probe. J. Biomed. Opt. 23, 121611.
  • Huang, F., 2018. Analysis of vascular biomarkers on retinal images for early eye disease detection.
  • Li, Z., Huang, F., Zhang, J., Dashtbozorg, B., Abbasi-Sureshjani, S., Sun, Y., Long, X., Yu, Q., 2018. B. ter Haar Romeny, and T. Tan. Multi-modal multi-vendor Retin. image Regist. Biomed,” Opt. Express 9, 410–422.
  • Li, J., Huang, F., Zhang, J., Dashtbozorg, B., Abbasi-Sureshjani, S., Sun, Y., 2018. Xi Long. Xinyi Wang, Fang Fang, Xuefei Lv, Dandan Zhang, Yu Sun, Shaoping Hu, Zhicheng Lin, Nian Xiong." Radiol. Indispens. Track. COVID-19." Diagnostic Interv. imaging 102, 69–75.
  • Huang, F., Dashtbozorg, B., Yeung, A.K.S., Zhang, J., Berendschot, T.T.J.M., ter Haar Romeny, B.M., 2017. A comparative study towards the establishment of an automatic retinal vessel width measurement technique, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-67561-9_26
  • Cheplygina, V., Moeskops, P., Veta, M., Dashtbozorg, B., Pluim, J.P.W., 2017. Exploring the Similarity of Medical Imaging Classification Problems, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-67534-3_7
  • Zhang, J., Chen, Y., Bekkers, E., Wang, M., Dashtbozorg, B., Romeny, B.M.T.H., 2017. Retinal vessel delineation using a brain-inspired wavelet transform and random forest. Pattern Recognit. 69. https://doi.org/10.1016/j.patcog.2017.04.008
  • Dashtbozorg, B., Zhang, J., Abbasi-Sureshjani, S., Huang, F., Ter Haar Romeny, B.M., 2017. Retinal health information and notification system (RHINO), in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2253839
  • Abbasi-Sureshjani, S., Dashtbozorg, B., ter Haar Romeny, B.M., Fleuret, F., 2017. Boosted exudate segmentation in retinal images using residual nets, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-67561-9_24
  • Mendonça, A.M., Remeseiro, B., Dashtbozorg, B., Campilho, A., 2017. Automatic and semi-automatic approaches for arteriolar-to-venular computation in retinal photographs, in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. https://doi.org/10.1117/12.2255096
  • Bozorg, B.D., Zhang, J., ter Haar Romeny, B.M., 2017. Retinal microaneurysms detection using local convergence index features. arXiv 1–14.
  • Abbasi-Sureshjani, S., 2017. Contextual and deep learning approaches for retinal image analysis.
  • Zhang, J., 2017. Multi-orientation analysis of retinal images for computer-aided diagnosis.
  • Huang, F., Dashtbozorg, B., Zhang, J., Bekkers, E., Abbasi-Sureshjani, S., Berendschot, T.T.J.M., Ter Haar Romeny, B.M., 2016. Reliability of Using Retinal Vascular Fractal Dimension as a Biomarker in the Diabetic Retinopathy Detection. J. Ophthalmol. 2016. https://doi.org/10.1155/2016/6259047
  • Dashtbozorg, B., Zhang, J., Huang, F., ter Haar Romeny, B.M., 2016. Automatic optic disc and fovea detection in retinal images using super-elliptical convergence index filters, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-41501-7_78
  • Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., Ter Haar Romeny, B.M., 2016. Robust Retinal Vessel Segmentation via Locally Adaptive Derivative Frames in Orientation Scores. IEEE Trans. Med. Imaging 35. https://doi.org/10.1109/TMI.2016.2587062
  • Abbasi-Sureshjani, S., Smit-Ockeloen, I., Bekkers, E., Dashtbozorg, B., Romeny, B.T.H., 2016. Automatic detection of vascular bifurcations and crossings in retinal images using orientation scores, in: Proceedings - International Symposium on Biomedical Imaging. https://doi.org/10.1109/ISBI.2016.7493241
  • ter Haar Romeny, B.M., Bekkers, E.J., Zhang, J., Abbasi-Sureshjani, S., Huang, F., Duits, R., Dashtbozorg, B., Berendschot, T.T.J.M., Smit-Ockeloen, I., Eppenhof, K.A.J., 2016. Brain-inspired algorithms for retinal image analysis. Mach. Vis. Appl. 27, 1117–1135.
  • Zhang, J., Bekkers, E., Abbasi-Sureshjani, S., Dashtbozorg, B., ter Haar Romeny, B., 2016. Bridging Disconnected Curvilinear Structures via Numerical Evolutions of Completion Process in Ophthalmologic Images, in: Proceedings of the Ophthalmic Medical Image Analysis International Workshop. University of Iowa.
  • Dashtbozorg, B., Abbasi-Sureshjani, S., Zhang, J., Huang, F., Bekkers, E.J., ter Haar Romenij, B.M., 2016. Infrastructure for retinal image analysis, in: 2016 Ophthalmic Medical Image Analysis Third International Workshop (OMIA 2016). pp. 105–112.
  • Dashtbozorg, B., Zhang, J., Huang, F., ter Haar Romenij, B.M., 2016. RetinaCheck: an interactive platform for retinal image analysis, in: 13th IEEE International Symposium on Biomedical Imaging (ISBI 2016).
  • Dashtbozorg, B., Mendonça, A.M., Campilho, A., 2015. Assessment of retinal vascular changes through arteriolar-to-venular ratio calculation, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-20801-5_36
  • Dashtbozorg, B., Mendonça, A.M., Campilho, A., 2015. Optic disc segmentation using the sliding band filter. Comput. Biol. Med. 56. https://doi.org/10.1016/j.compbiomed.2014.10.009
  • Zhang, J., Bekkers, E., Abbasi, S., Dashtbozorg, B., ter Haar Romeny, B., 2015. Robust and fast vessel segmentation via Gaussian derivatives in orientation scores, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-23231-7_48
  • Dashtbozorg, B., 2015. Advanced image analysis for the assessment of retinal vascular changes.
  • Huang, F., Zhang, J., Bekkers, E.J., Dashtbozorg, B., ter Haar Romeny, B.M., 2015. Stability analysis of fractal dimension in retinal vasculature, in: Proceedings of the Ophthalmic Medical Image Analysis International Workshop. University of Iowa.
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