Examinando por Autor "Rojas-Costa, Gonzalo M."

Mostrando 1 - 2 de 2
  • Miniatura
    Ítem
    Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study
    (John Wiley & Sons, 2026-08-19) Seo, Jieun; Ripart, Mathilde; Kaas, Helene; Kronlage, Cornelius; Sinclair, Ben; Vivash, Lucy; Courtney, Merran R.; O'Brien, Terence J.; Gopinath, Siby; Parasuram, Harilal; Kandemirli, Sedat; Alarab, Natally; Lai, Lillian; Likeman, Marcus; Zhang, Kai; Mo, Jiajie; Ciobotaru, Georgian; Galea, James; Sequeiros-Peggs, Philip; Hamandi, Khalid; Xie, Hua; Illapani, Venkata Sita Priyanka.; Gaillard, William D.; Cohen, Nathan T.; Weil, Alexander G.; Henrichon-Goulet, Florence; Lahlou, Kenza S.; Hadjinicolaou, Aristides; Ibáñez, Agustín; Rojas-Costa, Gonzalo M.; Urbach, Horst; Bücheler, Lara; Heers, Marcel; Valls Carbó, Adrián; Toledano, Rafael; Nobile, Giulia; Parodi, Costanza; Tortora, Domenico; Consales, Alessandro; Riva, Antonella; Severino, Mariasavina; Tisdall, Martin; D'Arco, Felice; Mankad, Kshitij; Chari, Aswin; Eriksson, Maria H.; Piper, Rory J.; Cross, J. Helen; Baldeweg, Torsten; González-Ortiz, Sofia; Pariente, Jose; Bargalló, Nuria; Liu, Yawu; Kälviäinen, Reetta; Barba, Carmen; Lenge, Matteo; Guerrini, Renzo; Iwasaki, Masaki; Sone, Daichi; Maki, Hiroyuki; Imokawa, Tomoki; Sato, Noriko; Jung, Julien; Sepulveda, Francisco; Mansilla, Daniel; Goycoolea, Andres; Lopez, Ingeborg; Napolitano, Antonio; De Benedictis, Alessandro; De Palma, Luca; Rossi-Espagnet, Maria Camilla; Kondylidis, Nikolaos; Gkiatis, Kostakis; Garganis, Kyriakos; Pepper, Joshua; Seri, Stefano; Duncan, John S.; Yasuda, Clarissa L.; Scárdua-Silva, Lucas; Alvim, Marina K. M.; Cendes, Fernando; Gennari, Antonio G.; O'Gorman Tuura, Ruth; Ramantani, Georgia; Josyula, Mariam; Stein, Joel; Sinha, Nishant; Davis, Kate; Hogan, R. Edward.; Maccotta, Luigi; Adler, Sophie; Wagstyl, Konrad
    Objective Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time-consuming and labor-intensive, and existing automated approaches exhibit variable segmentation accuracy, particularly on extratemporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. Methods The study included 1.5- and 3T postoperative three-dimensional T1-weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project (nsubjects = 969, 27 centers) and from the EPISURG dataset (n = 133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU-Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality-controlled, edited, and combined with the original 285 to train the final MELD-PostOp model (n = 965). A Stratified Test Cohort (n = 50) and Independent Test Cohort (n = 87) were withheld for model evaluation. Performance was evaluated using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters, and inference runtime, and compared against established tools (Epic-CHOP, ResectVol, and RESSEG). Results MELD-PostOp achieved a median DSC of .85 and HD95 of 3.61 on the combined test cohort, outperforming Epic-CHOP (DSC .69, HD95 9.67), ResectVol (DSC .66, HD95 15.05), and RESSEG (DSC .43, HD95 32.67), with significant improvements seen in both temporal and especially extratemporal resections. The model detected 98.5% (135/137) of resection cavities. MELD-PostOp runtime was 17 s per MRI, compared to 612 s (ResectVol), 3205 s (Epic-CHOP), and 4 s (RESSEG). MELD-PostOp performance remained high across clinical and imaging subgroups (median DSC > .8). Significance MELD-PostOp is an open-source research tool that provides an accurate, efficient, and generalizable solution for postoperative resection cavity segmentation using only postoperative MRI scans.
  • Miniatura
    Ítem
    Intracranial volume variation in Chinchorro mummies: a comparative study with prehispanic farmers and contemporary Chilean populations
    (Nature, 2025-11-25) Rojas-Costa, Gonzalo M.
    The Chinchorro culture inhabited the coastal Atacama Desert between 7,500 and 3,500 years BP, maintaining a hunter-gatherer-fisher lifestyle and practicing complex mortuary rituals. With the later adoption of agriculture, shifts in subsistence strategies and burial customs emerged. This study investigated long-term biological variation across three diachronic populations from northern Chile: Archaic-period Chinchorro individuals, pre-Hispanic agriculturalists, and contemporary Chileans. Using computed tomography (CT) and 3D reconstruction techniques, we analyzed intracranial volume (ICV) and estimated stature to assess morphological differences. The results show that both ICV and stature are significantly greater in the present-day Chilean population than in the pre-Hispanic groups. The average ICV was 1,321.26 cc in Chinchorro individuals, 1,336.57 cc in pre-Hispanic agriculturalists, and 1,481.22 cc in modern Chileans—representing a 12.05% increase between the earliest and most recent groups. Males and females exhibited 14.62% and 10.81% increases, respectively. Sexual dimorphism in the ICV was lower among agriculturalists (8.97%) than among Chinchorro (10.84%) and modern individuals (13.68%). Notably, the transition to agriculture did not result in significant changes in either ICV or stature. Instead, the marked increases observed in modern individuals have been associated primarily with improved nutrition, healthcare, and overall living conditions since the 20th century.