CatStep: Automated Cataract Surgical Phase Classification and Boundary Segmentation Leveraging Inflated 3D-Convolutional Neural Network Architectures and BigCat

Mahmoud, Ossama; Zhang, Han; Matton, Nicholas; Mian, Shahzad I.; Tannen, Bradford; Nallasamy, Nambi

doi:10.1016/j.xops.2023.100405

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…A typical cataract surgery can be broken into 11 active surgical phases: Paracentesis, Medication and Viscoelastic Insertion, Main Wound, Capsulorrhexis Initiation, Capsulorrhexis Formation, Hydrodissection, Phacoemulsification, Cortical Removal, Lens Insertion, Viscoelastic Removal, and Wound Closure. 2 These surgical steps involve different instrumentation and result in varying appearances of the pupil intraoperatively.…”

Section: Introductionmentioning

confidence: 99%

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery

Giap,

Srinivasan,

Mahmoud

et al. 2024

Preprint

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PurposePupillary instability is a known risk factor for complications in cataract surgery. This study aims to develop and validate an innovative and reliable computational framework for the automated assessment of pupil morphologic changes during the various phases of cataract surgery.DesignRetrospective surgical video analysis.SubjectsTwo hundred forty complete surgical video recordings, among which 190 surgeries were conducted without the use of pupil expansion devices and 50 were performed with the use of a pupil expansion device.MethodsThe proposed framework consists of three stages: feature extraction, deep learning (DL)-based anatomy recognition, and obstruction detection/compensation. In the first stage, surgical video frames undergo noise reduction using a tensor-based wavelet feature extraction method. In the second stage, DL-based segmentation models are trained and employed to segment the pupil, limbus, and palpebral fissure. In the third stage, obstructed visualization of the pupil is detected and compensated for using a DL-based algorithm. A dataset of 5,700 intraoperative video frames across 190 cataract surgeries in the BigCat database was collected for validating algorithm performance.Main Outcome MeasuresThe pupil analysis framework was assessed on the basis of segmentation performance for both obstructed and unobstructed pupils. Classification performance of models utilizing the segmented pupil time series to predict surgeon use of a pupil expansion device was also assessed.ResultsAn architecture based on the FPN model with VGG16 backbone integrated with the AWTFE feature extraction method demonstrated the highest performance in anatomy segmentation, with Dice coefficient of 96.52%. Incorporation of an obstruction compensation algorithm improved performance further (Dice 96.82%). Downstream analysis of framework output enabled the development of an SVM-based classifier that could predict surgeon usage of a pupil expansion device prior to its placement with 96.67% accuracy and AUC of 99.44%.ConclusionsThe experimental results demonstrate that the proposed framework 1) provides high accuracy in pupil analysis compared to human-annotated ground truth, 2) substantially outperforms isolated use of a DL segmentation model, and 3) can enable downstream analytics with clinically valuable predictive capacity.

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Section: Introductionmentioning

confidence: 99%

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery

Giap,

Srinivasan,

Mahmoud

et al. 2024

Preprint

Self Cite

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“…An alternative to this approach is to provide automated objective feedback using computational analysis of surgical video. 11,12 This could improve objectivity by separating the mentor-mentee relationship from the analysis of performance, while allowing for longitudinal tracking of performance characteristics. 13 Since the quality of the CCC depends at least in part on the morphological characteristics of the anterior capsulotomy, an important step in developing an automated tool for analysis of capsulotomy performance (and cataract surgery performance) is the detection of the capsulotomy and segmentation of the completed anterior capsulotomy in surgical video.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video

Zeng,

Giap,

Kahana

et al. 2024

OPTH

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Background The capsulorhexis is one of the most important and challenging maneuvers in cataract surgery. Automated analysis of the anterior capsulotomy could aid surgical training through the provision of objective feedback and guidance to trainees. Purpose To develop and evaluate a deep learning-based system for the automated identification and semantic segmentation of the anterior capsulotomy in cataract surgery video. Methods In this study, we established a BigCat-Capsulotomy dataset comprising 1556 video frames extracted from 190 recorded cataract surgery videos for developing and validating the capsulotomy recognition system. The proposed system involves three primary stages: video preprocessing, capsulotomy video frame classification, and capsulotomy segmentation. To thoroughly evaluate its efficacy, we examined the performance of a total of eight deep learning-based classification models and eleven segmentation models, assessing both accuracy and time consumption. Furthermore, we delved into the factors influencing system performance by deploying it across various surgical phases. Results The ResNet-152 model employed in the classification step of the proposed capsulotomy recognition system attained strong performance with an overall Dice coefficient of 92.21%. Similarly, the UNet model with the DenseNet-169 backbone emerged as the most effective segmentation model among those investigated, achieving an overall Dice coefficient of 92.12%. Moreover, the time consumption of the system was low at 103.37 milliseconds per frame, facilitating its application in real-time scenarios. Phase-wise analysis indicated that the Phacoemulsification phase (nuclear disassembly) was the most challenging to segment (Dice coefficient of 86.02%). Conclusion The experimental results showed that the proposed system is highly effective in intraoperative capsulotomy recognition during cataract surgery and demonstrates both high accuracy and real-time capabilities. This system holds significant potential for applications in surgical performance analysis, education, and intraoperative guidance systems.

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CatStep: Automated Cataract Surgical Phase Classification and Boundary Segmentation Leveraging Inflated 3D-Convolutional Neural Network Architectures and BigCat

Cited by 2 publications

References 18 publications

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery

A Computational Framework for Intraoperative Pupil Analysis in Cataract Surgery

Evaluation of Methods for Detection and Semantic Segmentation of the Anterior Capsulotomy in Cataract Surgery Video

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