{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:08:25Z","timestamp":1768925305802,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T00:00:00Z","timestamp":1623628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Regional Government of Madrid","award":["Y2018\/BIO-4826"],"award-info":[{"award-number":["Y2018\/BIO-4826"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>HyperSpectral (HS) images have been successfully used for brain tumor boundary detection during resection operations. Nowadays, these classification maps coexist with other technologies such as MRI or IOUS that improve a neurosurgeon\u2019s action, with their incorporation being a neurosurgeon\u2019s task. The project in which this work is framed generates an unified and more accurate 3D immersive model using HS, MRI, and IOUS information. To do so, the HS images need to include 3D information and it needs to be generated in real-time operating room conditions, around a few seconds. This work presents Graph cuts Reference depth estimation in GPU (GoRG), a GPU-accelerated multiview depth estimation tool for HS images also able to process YUV images in less than 5.5 s on average. Compared to a high-quality SoA algorithm, MPEG DERS, GoRG YUV obtain quality losses of \u22120.93 dB, \u22120.6 dB, and \u22121.96% for WS-PSNR, IV-PSNR, and VMAF, respectively, using a video synthesis processing chain. For HS test images, GoRG obtains an average RMSE of 7.5 cm, with most of its errors in the background, needing around 850 ms to process one frame and view. These results demonstrate the feasibility of using GoRG during a tumor resection operation.<\/jats:p>","DOI":"10.3390\/s21124091","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:26:01Z","timestamp":1623709561000},"page":"4091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["GoRG: Towards a GPU-Accelerated Multiview Hyperspectral Depth Estimation Tool for Medical Applications"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8767-6596","authenticated-orcid":false,"given":"Jaime","family":"Sancho","sequence":"first","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5731-5199","authenticated-orcid":false,"given":"Pallab","family":"Sutradhar","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3236-1236","authenticated-orcid":false,"given":"Gonzalo","family":"Rosa","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0280-3440","authenticated-orcid":false,"given":"Miguel","family":"Chavarr\u00edas","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2391-6586","authenticated-orcid":false,"given":"Angel","family":"Perez-Nu\u00f1ez","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0021-5808","authenticated-orcid":false,"given":"Rub\u00e9n","family":"Salvador","sequence":"additional","affiliation":[{"name":"CentraleSup\u00e9lec, CNRS, IETR, UMR 6164, 35700 Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3996-0554","authenticated-orcid":false,"given":"Alfonso","family":"Lagares","sequence":"additional","affiliation":[{"name":"Instituto de Investigaci\u00f3n Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-1511","authenticated-orcid":false,"given":"Eduardo","family":"Ju\u00e1rez","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2411-9132","authenticated-orcid":false,"given":"C\u00e9sar","family":"Sanz","sequence":"additional","affiliation":[{"name":"Research Center on Software Technologies and Multimedia Systems (CITSEM), Universidad Polit\u00e9cnica de Madrid (UPM), 28031 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/79.974718","article-title":"Hyperspectral image data analysis","volume":"19","author":"Landgrebe","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_2","unstructured":"Tian, J., Wang, S., Zhang, L., Wu, T., She, X., and Jiang, H. (2015, January 2\u20135). Evaluating different vegetation index for estimating lai of winter wheat using hyperspectral remote sensing data. Proceedings of the 2015 7thWorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhang, L., Tian, J., and Zhang, X. (2016, January 11\u201315). Sensitivity analysis for Chl-a retrieval of water body using hyperspectral remote sensing data with different spectral indicators. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729411"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Prasad, S., Priya, T., Cui, M., and Shah, S. (2016, January 21\u201324). Person re-identification with hyperspectral multi-camera systems\u2014A pilot study. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071801"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Platias, C., Kandylakis, Z., Panagou, E.Z., Nychas, G.E., and Karantzalos, K. (2018, January 23\u201326). Snapshot Multispectral and Hyperspectral Data Processing for Estimating Food Quality Parameters. Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2018.8747009"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kartakoullis, A., Kamilaris, A., Gonzalez, J., Serra, X., Gou, P., and Font, M. (2018, January 23\u201326). Hyperspectral Imaging for Assessing the Quality Attributes of Cured Pork Loin. Proceedings of the 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2018.8747235"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Noor, S.S.M., Michael, K., Marshall, S., Ren, J., Tschannerl, J., and Kao, F.J. (2016, January 23\u201325). The properties of the cornea based on hyperspectral imaging: Optical biomedical engineering perspective. Proceedings of the 2016 International Conference on Systems, Signals and Image Processing (IWSSIP), Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502710"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nakaya, D., Tomiyama, Y., Satori, S., Saegusa, M., Yoshida, T., Yokoi, A., and Kano, M. (2018, January 3\u20136). Development of High-Performance Pathological Diagnosis Software Using a Hyperspectral Camera. Proceedings of the 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Kuching Sarawak, Malaysia.","DOI":"10.1109\/IECBES.2018.8626666"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.19.9.096013","article-title":"Medical Hyperspectral Imaging: A Review","volume":"19","author":"Lu","year":"2014","journal-title":"J. Biomed. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Nathan, M., Kabatznik, A.S., and Mahmood, A. (2018, January 4\u20136). Hyperspectral imaging for cancer detection and classification. Proceedings of the 2018 3rd Biennial South African Biomedical Engineering Conference (SAIBMEC), Stellenbosch, South Africa.","DOI":"10.1109\/SAIBMEC.2018.8363180"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Weijtmans, P.J.C., Shan, C., Tan, T., Brouwer de Koning, S.G., and Ruers, T.J.M. (2019, January 8\u201311). A Dual Stream Network for Tumor Detection in Hyperspectral Images. Proceedings of the 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759566"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gopi, A., and Reshmi, C.S. (2017, January 22\u201324). A noninvasive cancer detection using hyperspectral images. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2017.8300122"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"39098","DOI":"10.1109\/ACCESS.2019.2904788","article-title":"In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection","volume":"7","author":"Fabelo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8485","DOI":"10.1109\/ACCESS.2020.2963939","article-title":"Towards Real-Time Computing of Intraoperative Hyperspectral Imaging for Brain Cancer Detection Using Multi-GPU Platforms","volume":"8","author":"Florimbi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"152316","DOI":"10.1109\/ACCESS.2019.2938708","article-title":"Parallel Implementations Assessment of a Spatial-Spectral Classifier for Hyperspectral Clinical Applications","volume":"7","author":"Lazcano","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kim, Y., Oh, J., Choi, J., and Kim, Y. (2015, January 2\u20135). Comparative analysis of the hyperspectral vegetatation index and radar vegetation index: A novel fusion vegetation index. Proceedings of the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2015.8075434"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hu, J., Ghamisi, P., Schmitt, A., and Zhu, X.X. (2016, January 21\u201324). Object based fusion of polarimetric SAR and hyperspectral imaging for land use classification. Proceedings of the 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA.","DOI":"10.1109\/WHISPERS.2016.8071752"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Marpu, P.R., and Martinez, S.S. (2015, January 2\u20135). Object-based fusion of hyperspectral and LiDAR data for classification of urban areas. Proceedings of the 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Tokyo, Japan.","DOI":"10.1109\/WHISPERS.2015.8075399"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tamilselvi, R., Nagaraj, A., Beham, M.P., and Sandhiya, M.B. (2020, January 27\u201328). BRAMSIT: A Database for Brain Tumor Diagnosis and Detection. Proceedings of the 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), Chennai, Tamilnadu, India.","DOI":"10.1109\/ICBSII49132.2020.9167530"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/TMI.2014.2354352","article-title":"Automatic Deformable MR-Ultrasound Registration for Image-Guided Neurosurgery","volume":"34","author":"Rivaz","year":"2015","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kahraman, S., Bacher, R., Uezato, T., Chanussot, J., and Tangel, A. (2019, January 24\u201326). LiDAR-Guided Reduction Of Spectral Variability in Hyperspectral Imagery. Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS.2019.8920932"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3926","DOI":"10.1109\/JSTARS.2015.2483678","article-title":"Ground-Based Panoramic Stereo Hyperspectral Imaging System With Multiband Stereo Matching","volume":"9","author":"Karaca","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Heide, N., Frese, C., Emter, T., and Petereit, J. (2018, January 7\u201310). Real- Time Hyperspectral Stereo Processing for the Generation of 3D Depth Information. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451194"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hamzah, R.A., Kadmin, A.F., Hamid, M.S., Abd Gani, S.F., Salam, S., and Mohamood, N. (2018, January 24\u201326). Development of Stereo Matching Algorithm Based on Guided Filter. Proceedings of the 2018 2nd International Conference on Smart Sensors and Application (ICSSA), Kuching, Malaysia.","DOI":"10.1109\/ICSSA.2018.8535846"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chai, Y., and Cao, X. (2018, January 12\u201314). Stereo matching algorithm based on joint matching cost and adaptive window. Proceedings of the 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China.","DOI":"10.1109\/IAEAC.2018.8577495"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xu, B., Zhao, S., Sui, X., and Hua, C. (2018, January 16\u201318). High-speed stereo matching algorithm for ultra-high resolution binocular image. Proceedings of the 2018 IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), Shenyang, China.","DOI":"10.1109\/AUTEEE.2018.8720762"},{"key":"ref_27","unstructured":"Zhang, J., Nezan, J., Pelcat, M., and Cousin, J. (2013, January 8\u201310). Real-time GPU-based local stereo matching method. Proceedings of the 2013 Conference on Design and Architectures for Signal and Image Processing, Cagliari, Italy."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"42030","DOI":"10.1109\/ACCESS.2018.2859445","article-title":"Real-Time Stereo Vision System: A Multi-Block Matching on GPU","volume":"6","author":"Chang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chai, Y., and Yang, F. (2018, January 25\u201327). Semi-Global Stereo Matching Algorithm Based on Minimum Spanning Tree. Proceedings of the 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China.","DOI":"10.1109\/IMCEC.2018.8469306"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rogge, S., Bonatto, D., Sancho, J., Salvador, R., Juarez, E., Munteanu, A., and Lafruit, G. (2019, January 11\u201312). MPEG-I Depth Estimation Reference Software. Proceedings of the 2019 International Conference on 3D Immersion (IC3D), Brussels, Belgium.","DOI":"10.1109\/IC3D48390.2019.8975995"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Randeberg, L.L., Denstedt, M., Paluchowski, L., Milanic, M., and Pukstad, B.S. (2013, January 5\u20136). Combined hyperspectral and 3D characterization of non-healing skin ulcers. Proceedings of the 2013 Colour and Visual Computing Symposium (CVCS), Gjovik, Sudan.","DOI":"10.1109\/CVCS.2013.6626271"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Vineet, V., and Narayanan, P.J. (2008, January 24\u201326). CUDA cuts: Fast graph cuts on the GPU. Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Anchorage, AK, USA.","DOI":"10.1109\/CVPRW.2008.4563095"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TIP.2014.2378060","article-title":"JF-Cut: A Parallel Graph Cut Approach for Large-Scale Image and Video","volume":"24","author":"Peng","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","first-page":"1777","article-title":"New Hyperspectral Discrimination Measure for Spectral Characterization","volume":"43","author":"Du","year":"2004","journal-title":"Opt. Eng. OPT ENG"},{"key":"ref_36","unstructured":"Sancho, J., Senoh, T., Rogge, S., Bonatto, D., Salvador, R., Ju\u00e1rez, E., Munteanu, A., and Lafruit, G. MPEG Contribution: Exploration Experiments for MPEG-I Visual: 6DoF [M53257], ISO\/IEC JTC1\/SC29\/WG11."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Schenkel, A., Bonatto, D., Fachada, S., Guillaume, H., and Lafruit, G. (2018, January 5\u20136). Natural Scenes Datasets for Exploration In 6DOF Navigation. Proceedings of the 2018 International Conference on 3D Immersion (IC3D), Brussels, Belgium.","DOI":"10.1109\/IC3D.2018.8657865"},{"key":"ref_38","unstructured":"(2019). MPEG Contribution: Exploration Experiments for MPEG-I Visual: 6DoF [N18790], ISO\/IEC JTC1\/SC29\/WG11."},{"key":"ref_39","unstructured":"Boissonade, P., and Jung, J. (2019). MPEG Contribution: Versatile View Synthesizer 2.0 (VVS 2.0) manual [w18172], ISO\/IEC JTC1\/SC29\/WG11."},{"key":"ref_40","unstructured":"(2018). MPEG Contribution: WS-PSNR Software Manual [w18069], ISO\/IEC JTC1\/SC29\/WG11."},{"key":"ref_41","unstructured":"(2019). MPEG Contribution: Software manual of IV-PSNR for Immersive Video [w18709], ISO\/IEC JTC1\/SC29\/WG11."},{"key":"ref_42","unstructured":"(2021, June 10). Perceptual Video Quality ASSESSMENT Based on Multi-Method Fusion. Available online: https:\/\/github.com\/Netflix\/vmaf."},{"key":"ref_43","unstructured":"NVIDIA (2021, June 10). GPGPU 2015: High Performance Computing with CUDA. Available online: http:\/\/gpu.cs.uct.ac.za\/Slides\/CUDA-4x1_ujaldon.pdf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"382-1","DOI":"10.2352\/ISSN.2470-1173.2020.13.ERVR-382","article-title":"RaViS: Real-time accelerated View Synthesizer for immersive video 6DoF VR","volume":"2020","author":"Bonatto","year":"2020","journal-title":"Electron. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Stankiewicz, O., Doma\u0144ski, M., Dziembowski, A., Grzelka, A., Mieloch, D., and Samelak, J. (2018). A Free-viewpoint Television System for Horizontal Virtual Navigation. IEEE Trans. Multimed., 1.","DOI":"10.1109\/TMM.2018.2790162"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4091\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:14:06Z","timestamp":1760163246000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/12\/4091"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,14]]},"references-count":45,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["s21124091"],"URL":"https:\/\/doi.org\/10.3390\/s21124091","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,14]]}}}