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While deep learning models have shown promise in this task, their predictions often suffer from frame-to-frame inconsistency. This study addresses this issue by proposing ConsisTNet, a novel spatio-temporal model designed to improve prediction stability.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Methods<\/jats:bold>\n            <\/jats:title>\n            <jats:p>ConsisTNet leverages spatio-temporal features extracted from consecutive frames to provide both temporally and spatially consistent predictions, addressing the limitations of single-frame approaches. We employ a semi-supervised strategy, utilizing ground-truth label tracking for pseudo-label generation through label propagation. Consistency is assessed by comparing predictions across consecutive frames using predicted label tracking. The model is optimized and accelerated using TensorRT for real-time intraoperative guidance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Results<\/jats:bold>\n            <\/jats:title>\n            <jats:p>Compared to previous state-of-the-art models, ConsisTNet significantly improves prediction consistency across video frames while maintaining high accuracy in segmentation and landmark detection. Specifically, segmentation consistency is improved by 4.56 and 9.45% in IoU for the two segmentation regions, and landmark detection consistency is enhanced with a 43.86% reduction in mean distance error. The accelerated model achieves an inference speed of 202 frames per second (FPS) with 16-bit floating point (FP16) precision, enabling real-time intraoperative guidance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>\n              <jats:bold>Conclusion<\/jats:bold>\n            <\/jats:title>\n            <jats:p>ConsisTNet demonstrates significant improvements in spatio-temporal consistency of anatomical localization during endoscopic pituitary surgery, providing more stable and reliable real-time surgical assistance.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03369-2","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:29:00Z","timestamp":1745926140000},"page":"1239-1248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["ConsisTNet: a spatio-temporal approach for consistent anatomical localization in endoscopic pituitary surgery"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9225-6318","authenticated-orcid":false,"given":"Zhehua","family":"Mao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adrito","family":"Das","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9213-2550","authenticated-orcid":false,"given":"Danyal Z.","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon C.","family":"Williams","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John G.","family":"Hanrahan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani J.","family":"Marcus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"3369_CR1","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.wneu.2019.03.048","volume":"127","author":"LM Cavallo","year":"2019","unstructured":"Cavallo LM, Somma T, Solari D, Iannuzzo G, Frio F, Baiano C, Cappabianca P (2019) Endoscopic endonasal transsphenoidal surgery: history and evolution. 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HJM is employed by Panda Surgical and holds shares in the company.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Ethic approval (REC reference: 21\/SW\/0027) was granted for the data collected for this project via a regional ethics committee (IRAS 271696).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed written patient consent was obtained for the data used for AI model development.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}