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    <pageNumber>31</pageNumber>
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    <volume>109</volume>
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    <title language="eng">Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge</title>
    <abstract language="eng">Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context – such as the current procedural phase – has emerged as a promising strategy to improve robustness and interpretability.&#13;
To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures.&#13;
We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding.</abstract>
    <parentTitle language="eng">Medical Image Analysis</parentTitle>
    <identifier type="issn">1361-8415</identifier>
    <identifier type="doi">10.1016/j.media.2026.103945</identifier>
    <note>Corresponding author der OTH Regensburg: Tobias Rueckert&#13;
&#13;
Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: &#13;
https://opus4.kobv.de/opus4-oth-regensburg/solrsearch/index/search/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/2507.16559</note>
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    <enrichment key="CorrespondingAuthor">Tobias Rueckert</enrichment>
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    <author>Tobias Rueckert</author>
    <author>David Rauber</author>
    <author>Raphaela Maerkl</author>
    <author>Leonard Klausmann</author>
    <author>Suemeyye R. Yildiran</author>
    <author>Max Gutbrod</author>
    <author>Danilo Weber Nunes</author>
    <author>Alvaro Fernandez Moreno</author>
    <author>Imanol Luengo</author>
    <author>Danail Stoyanov</author>
    <author>Nicolas Toussaint</author>
    <author>Enki Cho</author>
    <author>Hyeon Bae Kim</author>
    <author>Oh Sung Choo</author>
    <author>Ka Young Kim</author>
    <author>Seong Tae Kim</author>
    <author>Gonçalo Arantes</author>
    <author>Kehan Song</author>
    <author>Jianjun Zhu</author>
    <author>Junchen Xiong</author>
    <author>Tingyi Lin</author>
    <author>Shunsuke Kikuchi</author>
    <author>Hiroki Matsuzaki</author>
    <author>Atsushi Kouno</author>
    <author>João Renato Ribeiro Manesco</author>
    <author>João Paulo Papa</author>
    <author>Tae-Min Choi</author>
    <author>Tae Kyeong Jeong</author>
    <author>Juyoun Park</author>
    <author>Oluwatosin Alabi</author>
    <author>Meng Wei</author>
    <author>Tom Vercauteren</author>
    <author>Runzhi Wu</author>
    <author>Mengya Xu</author>
    <author>An Wang</author>
    <author>Long Bai</author>
    <author>Hongliang Ren</author>
    <author>Amine Yamlahi</author>
    <author>Jakob Hennighausen</author>
    <author>Lena Maier-Hein</author>
    <author>Satoshi Kondo</author>
    <author>Satoshi Kasai</author>
    <author>Kousuke Hirasawa</author>
    <author>Shu Yang</author>
    <author>Yihui Wang</author>
    <author>Hao Chen</author>
    <author>Santiago Rodríguez</author>
    <author>Nicolás Aparicio</author>
    <author>Leonardo Manrique</author>
    <author>Christoph Palm</author>
    <author>Dirk Wilhelm</author>
    <author>Hubertus Feussner</author>
    <author>Daniel Rueckert</author>
    <author>Stefanie Speidel</author>
    <author>Sahar Nasirihaghighi</author>
    <author>Yasmina Al Khalil</author>
    <author>Yiping Li</author>
    <author>Pablo Arbeláez</author>
    <author>Nicolás Ayobi</author>
    <author>Olivia Hosie</author>
    <author>Juan Camilo Lyons</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Surgical phase recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Instrument keypoint estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Instrument instance segmentation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Robot-assisted surgery</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="institutes" number="RCHST">Regensburg Center of Health Sciences and Technology - RCHST</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
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    <collection role="othforschungsschwerpunkt" number="">Digitale Transformation</collection>
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    <id>8869</id>
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    <publishedYear>2025</publishedYear>
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    <language>eng</language>
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    <title language="eng">PhaKIR Dataset - Surgical Procedure Phase, Keypoint, and Instrument Recognition [Data set]</title>
    <abstract language="eng">Note: A script for extracting the individual frames from the video files while preserving the challenge-compliant directory structure and frame-to-mask naming conventions is available on GitHub and can be accessed here: https://github.com/remic-othr/PhaKIR_Dataset.&#13;
&#13;
The dataset is described in the following publications: &#13;
&#13;
    Rueckert, Tobias et al.: Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge. arXiv preprint, https://arxiv.org/abs/2507.16559. 2025.&#13;
    Rueckert, Tobias et al.: Video Dataset for Surgical Phase, Keypoint, and Instrument Recognition in Laparoscopic Surgery (PhaKIR). arXiv preprint, https://arxiv.org/abs/2511.06549. 2025.&#13;
&#13;
The proposed dataset was used as the training dataset in the PhaKIR challenge (https://phakir.re-mic.de/) as part of EndoVis-2024 at MICCAI 2024 and consists of eight real-world videos of human cholecystectomies ranging from 23 to 60 minutes in duration. The procedures were performed by experienced physicians, and the videos were recorded in three hospitals. In addition to existing datasets, our annotations provide pixel-wise instance segmentation masks of surgical instruments for a total of 19 categories, coordinates of relevant instrument keypoints (instrument tip(s), shaft-tip transition, shaft), both at an interval of one frame per second, and specifications regarding the intervention phases for a total of eight different phase categories for each individual frame in one dataset and thus comprehensively cover instrument localization and the context of the operation. Furthermore, the provision of the complete video sequences offers the opportunity to include the temporal information regarding the respective tasks and thus further optimize the resulting methods and outcomes.</abstract>
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    <author>Tobias Rueckert</author>
    <author>David Rauber</author>
    <author>Leonard Klausmann</author>
    <author>Max Gutbrod</author>
    <author>Daniel Rueckert</author>
    <author>Hubertus Feussner</author>
    <author>Dirk Wilhelm</author>
    <author>Christoph Palm</author>
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    <id>8567</id>
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    <issue/>
    <volume>20</volume>
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    <publisherName>Springer Nature</publisherName>
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    <title language="eng">Enhancing generalization in zero-shot multi-label endoscopic instrument classiﬁcation</title>
    <abstract language="eng">Purpose &#13;
Recognizing previously unseen classes with neural networks is a signiﬁcant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classiﬁcation is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings.&#13;
&#13;
Methods &#13;
This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies.&#13;
&#13;
Results &#13;
The results demonstrate that combining sentence embeddings and z-score normalization signiﬁcantly improves model performance. For unseen classes, the AUROC improves from 43.9% to 64.9%, and the multi-label accuracy from 26.1% to 79.5%. Overall performance measured across both seen and unseen classes improves from 49.3% to 64.9% in AUROC and from 37.3% to 65.1% in multi-label accuracy, highlighting the effectiveness of our approach.&#13;
&#13;
Conclusion &#13;
These ﬁndings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.</abstract>
    <parentTitle language="eng">International Journal of Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-025-03439-5</identifier>
    <identifier type="urn">urn:nbn:de:bvb:898-opus4-85674</identifier>
    <note>Corresponding author der OTH Regensburg: Raphaela Maerkl</note>
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    <author>Max Gutbrod</author>
    <author>Danilo Weber Nunes</author>
    <author>Christoph Palm</author>
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      <value>Multi-label classiﬁcation</value>
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      <value>Surgical instruments</value>
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    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
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    <title language="eng">DIY challenge blueprint: from organization to technical realization in biomedical image analysis</title>
    <abstract language="eng">Biomedical image analysis challenges have become the de facto standard for publishing new datasets and benchmarking diﬀerent state-of-the-art algorithms. Most challenges use commercial cloud-based platforms, which can limit custom options and involve disadvantages such as reduced data control and increased costs for extended functionalities. In contrast, Do-It-Yourself (DIY) approaches have the capability to emphasize reliability, compliance, and custom features, providing a solid basis for low-cost, custom designs in self-hosted systems. Our approach emphasizes cost eﬃciency, improved data sovereignty, and strong compliance with regulatory frameworks, such as the GDPR. This paper presents a blueprint for DIY biomedical imaging challenges, designed to provide institutions with greater autonomy over their challenge infrastructure. Our approach comprehensively addresses both organizational and technical dimensions, including key user roles, data management strategies, and secure, eﬃcient workﬂows. Key technical contributions include a modular, containerized infrastructure based on Docker, integration of open-source identity management, and automated solution evaluation workﬂows. Practical deployment guidelines are provided to facilitate implementation and operational stability. The feasibility and adaptability of the proposed framework are demonstrated through the MICCAI 2024 PhaKIR challenge with multiple international teams submitting and validating their solutions through our self-hosted platform. This work can be used as a baseline for future self-hosted DIY implementations and our results encourage further studies in the area of biomedical image analysis challenges.</abstract>
    <parentTitle language="eng">Medical Image Computing and Computer Assisted Intervention - MICCAI 2025 ; Proceedings Part XI</parentTitle>
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    <title language="eng">Abstract: DIY Challenge Blueprint</title>
    <abstract language="eng">The high cost of challenge platforms prevents many people from organizing their own competitions. The do-it-yourself (DIY) challenge blueprint [1] allows you to host your own biomedical AI benchmark challenge. Our DIY approach circumvents the current constraints of commercial challenge platforms. A sovereign, extensible and cost-efficient deployment is provided via containerised, identity-managed and reproducible pipelines. Focus lies on GDPR-compliant hosting via infrastructure-as-code, automated evaluation, modular orchestration, and role-based identity and access management. The framework integrates Docker-based execution and standardised interfaces for task definitions, dataset curation and evaluation. All in all it is designed to be flexible and modular, as demonstrated in the MICCAI 2024 PhaKIR challenge [2, 3]. In this case study, different medical tasks on a multicentre laparoscopic dataset with framewise labels for phases and spatial annotations for instruments across fulllength videos were supported. This case study empirically validates the DIY challenge blueprint as a reproducible and customizable challenge-hosting infrastructure. The full code can be found at https://github.com/remic-othr/PhaKIR_DIY.</abstract>
    <parentTitle language="eng">Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Lübeck March 15-17, 2026</parentTitle>
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