MIcro-surgical anastomose workflow recognition challenge report

Huaulmé, Arnaud; Sarıkaya, Duygu; Mut, Kévin Le; Despinoy, Fabien; Long, Yonghao; Dou, Qi; Chng, Chin-Boon; Lin, Wen Wei; Kondo, Satoshi; Bravo-Sánchez, Laura; Arbeláez, Pablo; Reiter, Wolfgang; Mitsuishi, Manoru; Harada, Kensuke; Jannin, Pierre

doi:10.1016/j.cmpb.2021.106452

Cited by 21 publications

(15 citation statements)

References 22 publications

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“…The computer-assisted intervention community releases different challenges every year, beginning with the M2CAI Miccai workflow challenge [9] in 2016, [19] and the introduction of the Endovis 1 grand challenges in 2017. Since then many different Endovis challenges on surgical workflow recognition have been conducted including the SensorOR, Cataracts workflow, MISAW [20] and HeiChole [21] challenges. We strongly believe that challenges are a fundamental tool to identify the best solution for a surgical workflow task.…”

Section: Related Workmentioning

confidence: 99%

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

Czempiel¹,

Sharghi²,

Paschali³

et al. 2022

Preprint

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Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different works for the internal analysis have been proposed with the combination of a frame-level and an additional temporal model to address the temporal ambiguities between different workflow phases. For the External recognition task, Clip-level methods are in the focus of researchers targeting the local ambiguities present in the OR scene. In this work we evaluate combinations of different model architectures for the task of surgical workflow recognition to provide a fair comparison of the methods for both Internal and External analysis. We show that methods designed for the Internal analysis can be transferred to the external task with comparable performance gains for different architectures.

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Section: Related Workmentioning

confidence: 99%

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

Czempiel¹,

Sharghi²,

Paschali³

et al. 2022

Preprint

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“…Individual phase lengths and phase occurrence patterns vary between surgeries and are, therefore, subject to further analysis. Additional terminology has developed for more low-level events than phases, like steps and actions [ 6 , 7 ], introducing hierarchical relations between the events. Neumuth et al refer to the visualization of surgical workflows as one of multiple levels of abstraction of surgical procedures [ 8 ] and proposed a timeline-based and logic-oriented visualization of such event-based information [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

“…In them, 21 different action classes were annotated [ 14 ]. In contrast, MISAW comprises video data, kinematic data, and workflow annotations [ 6 ]. The annotations consist of hierarchically ordered events: phases > steps > activities .…”

Section: Related Workmentioning

confidence: 99%

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Interactive visual exploration of surgical process data

et al. 2022

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Purpose Integrated operating rooms provide rich sources of temporal information about surgical procedures, which has led to the emergence of surgical data science. However, little emphasis has been put on interactive visualization of such temporal datasets to gain further insights. Our goal is to put heterogeneous data sequences in relation to better understand the workflows of individual procedures as well as selected subsets, e.g., with respect to different surgical phase distributions and surgical instrument usage patterns. Methods We developed a reusable web-based application design to analyze data derived from surgical procedure recordings. It consists of aggregated, synchronized visualizations for the original temporal data as well as for derived information, and includes tailored interaction techniques for selection and filtering. To enable reproducibility, we evaluated it across four types of surgeries from two openly available datasets (HeiCo and Cholec80). User evaluation has been conducted with twelve students and practitioners with surgical and technical background. Results The evaluation showed that the application has the complexity of an expert tool (System Usability Score of 57.73) but allowed the participants to solve various analysis tasks correctly (78.8% on average) and to come up with novel hypotheses regarding the data. Conclusion The novel application supports postoperative expert-driven analysis, improving the understanding of surgical workflows and the underlying datasets. It facilitates analysis across multiple synchronized views representing information from different data sources and, thereby, advances the field of surgical data science.

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“…Majority of methods are video-based only [17,18], or kinematic-based only [3,19]. Only few studies using both modalities to performed the workflow recognition [20,21,22]. Except Long et.al.…”

Section: Introductionmentioning

confidence: 99%

PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?

Huaulmé¹,

Harada²,

Nguyen³

et al. 2022

Preprint

Self Cite

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Background and Objective: Context-aware computer-assisted surgical systems require real time automatic and accurate surgical workflow recognition. Since several years, video has been the most common modality used to develop such methods. With the democratization of robotic-assisted surgery and segmentation method, new modalities are now accessible, such as kinematics. Some previous works used these new modalities as input of their models, but the added value of these modalities is rarely studied. This paper presents the design and results of the "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalities in order to study their added value. Methods: The PETRAW challenge provided a data set of 150 peg transfer sequences performed on a virtual simulator. This data set was composed of videos, kinematics, semantic segmentation, and workflow annotations which described the sequences at three different granularity levels: phase, step, and activity. Five tasks were proposed to the participants: three of them were related to the recognition of all granularities with one of the available modalities, while the others addressed the recognition with a combination of modalities. Average application-dependent balanced accuracy (AD-Accuracy) was used as evaluation metric to take unbalanced classes into account and because it is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task and four of them in all tasks. Best results are obtained with the use of the video and the kinematics data with an AD-Accuracy between 93% and 90% for the four teams who participated in all tasks. Conclusion:The improvement between video/kinematic-based methods and the uni-modality ones was significant for all of the teams. However, the difference in testing execution time between the video/kinematic-based and the kinematic-based methods has to be taken into consideration. Is it relevant to spend 20 to 200 times more computing time for less than 3% of improvement? The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.

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MIcro-surgical anastomose workflow recognition challenge report

Cited by 21 publications

References 22 publications

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study

Interactive visual exploration of surgical process data

PEg TRAnsfer Workflow recognition challenge report: Does multi-modal data improve recognition?

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