Fast instruments and tissues segmentation of micro-neurosurgical scene using high correlative non-local network

Luo, Yu-Wen; Chen, Haiyong; Li, Zhen; Liu, Weipeng; Wang, Ke; Zhang, Li; Fu, Pan; Yue, Wen-Qian; Bian, Gui‐Bin

doi:10.1016/j.compbiomed.2022.106531

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Li et al [ 19 ] implemented IB-TransUNet, merging the Information Bottleneck and Transformer into the U-Net model, and in [ 20 ], they proposed the MultiIB-TransUNet architecture. Some more recent architectures include High Correlative Non-Local Network (HCNNet), Bilateral Segmentation Network (BiSeNet V3), Contoured Convolutional Transformer (CCTrans), Cross-Convolutional Transformer Network (C 2 Former), Double-stage Codec Attention Network (DSCA-Net), and Medical Vision Transformer (MedViT) [ 21 , 22 , 23 , 24 , 25 , 26 ]. Additionally, specific architectures have been designed for the processing of 3D medical images [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance

Urrea,

Garcia-Garcia,

Kern

2024

Biomedicines

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This article addresses the semantic segmentation of laparoscopic surgery images, placing special emphasis on the segmentation of structures with a smaller number of observations. As a result of this study, adjustment parameters are proposed for deep neural network architectures, enabling a robust segmentation of all structures in the surgical scene. The U-Net architecture with five encoder–decoders (U-Net5ed), SegNet-VGG19, and DeepLabv3+ employing different backbones are implemented. Three main experiments are conducted, working with Rectified Linear Unit (ReLU), Gaussian Error Linear Unit (GELU), and Swish activation functions. The applied loss functions include Cross Entropy (CE), Focal Loss (FL), Tversky Loss (TL), Dice Loss (DiL), Cross Entropy Dice Loss (CEDL), and Cross Entropy Tversky Loss (CETL). The performance of Stochastic Gradient Descent with momentum (SGDM) and Adaptive Moment Estimation (Adam) optimizers is compared. It is qualitatively and quantitatively confirmed that DeepLabv3+ and U-Net5ed architectures yield the best results. The DeepLabv3+ architecture with the ResNet-50 backbone, Swish activation function, and CETL loss function reports a Mean Accuracy (MAcc) of 0.976 and Mean Intersection over Union (MIoU) of 0.977. The semantic segmentation of structures with a smaller number of observations, such as the hepatic vein, cystic duct, Liver Ligament, and blood, verifies that the obtained results are very competitive and promising compared to the consulted literature. The proposed selected parameters were validated in the YOLOv9 architecture, which showed an improvement in semantic segmentation compared to the results obtained with the original architecture.

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Section: Introductionmentioning

confidence: 99%

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance

Urrea,

Garcia-Garcia,

Kern

2024

Biomedicines

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Demonstrating the successful application of synthetic learning in spine surgery for training multi–center models with increased patient privacy

Schonfeld,

Veeravagu

2023

Sci Rep

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From real–time tumor classification to operative outcome prediction, applications of machine learning to neurosurgery are powerful. However, the translation of many of these applications are restricted by the lack of “big data” in neurosurgery. Important restrictions in patient privacy and sharing of imaging data reduce the diversity of the datasets used to train resulting models and therefore limit generalizability. Synthetic learning is a recent development in machine learning that generates synthetic data from real data and uses the synthetic data to train downstream models while preserving patient privacy. Such an approach has yet to be successfully demonstrated in the spine surgery domain. Spine radiographs were collected from the VinDR–SpineXR dataset, with 1470 labeled as abnormal and 2303 labeled as normal. A conditional generative adversarial network (GAN) was trained on the radiographs to generate a spine radiograph and normal/abnormal label. A modified conditional GAN (SpineGAN) was trained on the same task. A convolutional neural network (CNN) was trained using the real data to label abnormal radiographs. A CNN was trained to label abnormal radiographs using synthetic images from the GAN and in a separate experiment from SpineGAN. Using the real radiographs, an AUC of 0.856 was achieved in abnormality classification. Training on synthetic data generated by the standard GAN (AUC of 0.814) and synthetic data generated by our SpineGAN (AUC of 0.830) resulted in similar classifier performance. SpineGAN generated images with higher FID and lower precision scores, but with higher recall and increased performance when used for synthetic learning. The successful application of synthetic learning was demonstrated in the spine surgery domain for the classification of spine radiographs as abnormal or normal. A modified domain–relevant GAN is introduced for the generation of spine images, evidencing the importance of domain–relevant generation techniques in synthetic learning. Synthetic learning can allow neurosurgery to use larger and more diverse patient imaging sets to train more generalizable algorithms with greater patient privacy.

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Li,

Deng,

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Fast instruments and tissues segmentation of micro-neurosurgical scene using high correlative non-local network

Cited by 4 publications

References 18 publications

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance

Improving Surgical Scene Semantic Segmentation through a Deep Learning Architecture with Attention to Class Imbalance

Demonstrating the successful application of synthetic learning in spine surgery for training multi–center models with increased patient privacy

Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

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