Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study

Kolbinger, Fiona R.; Bodenstedt, Sebastian; Carstens, Matthias; Leger, Stefan; Krell, Stefanie; Rinner, Franziska M.; Nielen, Thomas P.; Kirchberg, Johanna; Fritzmann, Johannes; Weitz, Jürgen; Distler, Marius; Speidel, Stefanie

doi:10.1016/j.ejso.2023.106996

Cited by 11 publications

(3 citation statements)

References 32 publications

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“…encoder. The encoder is represented as x e in Eq (2). The output from the encoder x e is directly passed to the main decoder.…”

Section: Plos Onementioning

confidence: 99%

“…Robotic surgery, computer aided diagnosis, targeted radiation therapy require meticulous segmentation of affected organ from adjacent organs [1][2][3][4][5]. The authors of [6] examined the evolution of automatic multi-organ segmentation techniques, comparing traditional methods with deep learning approaches and found that deep learning methods consistently outperformed traditional approaches, indicating their superior efficiency in segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

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UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Hassan,

Mondal,

Ahamed

2024

PLoS ONE

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Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network’s ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

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“…encoder. The encoder is represented as x e in Eq (2). The output from the encoder x e is directly passed to the main decoder.…”

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Hassan,

Mondal,

Ahamed

2024

PLoS ONE

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“…video data from laparoscopic or open surgeries) has focused on classifying images with respect to the presence and/or location of previously annotated surgical instruments or anatomical structures 9 – 13 or on analysis of surgical proficiency 14 – 16 based on recorded procedures. However, almost all research endeavors in the field of computer vision in laparoscopic surgery have concentrated on preclinical stages, and to date, no AI model based on intraoperative surgical imaging data could demonstrate a palpable clinical benefit 17 , 18 . Among the studies closest to clinical application are recent works on the identification of instruments and hepatobiliary anatomy during cholecystectomy for automated assessment of the critical view of safety 13 and on the automated segmentation of safe and unsafe preparation zones during cholecystectomy 19 .…”

Section: Introductionmentioning

confidence: 99%

Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise – an experimental study

Kolbinger

Rinner

Jenke

et al. 2023

International Journal of Surgery

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Background: Lack of anatomy recognition represents a clinically relevant risk in abdominal surgery. Machine learning (ML) methods can help identify visible patterns and risk structures, however, their practical value remains largely unclear. Materials and Methods: Based on a novel dataset of 13195 laparoscopic images with pixel-wise segmentations of eleven anatomical structures, we developed specialized segmentation models for each structure and combined models for all anatomical structures using two state-of-the-art model architectures (DeepLabv3 and SegFormer), and compared segmentation performance of algorithms to a cohort of 28 physicians, medical students, and medical laypersons using the example of pancreas segmentation. Results: Mean Intersection-over-Union for semantic segmentation of intraabdominal structures ranged from 0.28 to 0.83 and from 0.23 to 0.77 for the DeepLabv3-based structure-specific and combined models, and from 0.31 to 0.85 and from 0.26 to 0.67 for the SegFormer-based structure-specific and combined models, respectively. Both the structure-specific and the combined DeepLabv3-based models are capable of near-real-time operation, while the SegFormer-based models are not. All four models outperformed at least 26 out of 28 human participants in pancreas segmentation. Conclusions: These results demonstrate that ML methods have the potential to provide relevant assistance in anatomy recognition in minimally-invasive surgery in near-real-time. Future research should investigate the educational value and subsequent clinical impact of respective assistance systems.

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Der digitale Operationssaal

Wierick,

Schulze,

Bodenstedt

et al. 2024

Chirurgie

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Artificial Intelligence for context-aware surgical guidance in complex robot-assisted oncological procedures: An exploratory feasibility study

Cited by 11 publications

References 32 publications

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Anatomy segmentation in laparoscopic surgery: comparison of machine learning and human expertise – an experimental study

Der digitale Operationssaal

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