Segmentation Of Surgical Instruments Research Articles

Enhancing surgical instrument segmentation: integrating vision transformer insights with adapter.

In surgical image segmentation, a major challenge is the extensive time and resources required to gather large-scale annotated datasets. Given the scarcity of annotated data in this field, our work aims to develop a model that achieves competitive performance with training on limited datasets, while also enhancing model robustness in various surgical scenarios. We propose a method that harnesses the strengths of pre-trained Vision Transformers (ViTs) and data efficiency of convolutional neural networks (CNNs). Specifically, we demonstrate how a CNN segmentation model can be used as a lightweight adapter for a frozen ViT feature encoder. Our novel feature adapter uses cross-attention modules that merge the multiscale features derived from the CNN encoder with feature embeddings from ViT, ensuring integration of the global insights from ViT along with local information from CNN. Extensive experiments demonstrate our method outperforms current models in surgical instrument segmentation. Specifically, it achieves superior performance in binary segmentation on the Robust-MIS 2019 dataset, as well as in multiclass segmentation tasks on the EndoVis 2017 and EndoVis 2018 datasets. It also showcases remarkable robustness through cross-dataset validation across these 3 datasets, along with the CholecSeg8k and AutoLaparo datasets. Ablation studies based on the datasets prove the efficacy of our novel adapter module. In this study, we presented a novel approach integrating ViT and CNN. Our unique feature adapter successfully combines the global insights of ViT with the local, multi-scale spatial capabilities of CNN. This integration effectively overcomes data limitations in surgical instrument segmentation. The source code is available at: https://github.com/weimengmeng1999/AdapterSIS.git .

SurgNet: Self-Supervised Pretraining With Semantic Consistency for Vessel and Instrument Segmentation in Surgical Images.

Blood vessel and surgical instrument segmentation is a fundamental technique for robot-assisted surgical navigation. Despite the significant progress in natural image segmentation, surgical image-based vessel and instrument segmentation are rarely studied. In this work, we propose a novel self-supervised pretraining method (SurgNet) that can effectively learn representative vessel and instrument features from unlabeled surgical images. As a result, it allows for precise and efficient segmentation of vessels and instruments with only a small amount of labeled data. Specifically, we first construct a region adjacency graph (RAG) based on local semantic consistency in unlabeled surgical images and use it as a self-supervision signal for pseudo-mask segmentation. We then use the pseudo-mask to perform guided masked image modeling (GMIM) to learn representations that integrate structural information of intraoperative objectives more effectively. Our pretrained model, paired with various segmentation methods, can be applied to perform vessel and instrument segmentation accurately using limited labeled data for fine-tuning. We build an Intraoperative Vessel and Instrument Segmentation (IVIS) dataset, comprised of ~3 million unlabeled images and over 4,000 labeled images with manual vessel and instrument annotations to evaluate the effectiveness of our self-supervised pretraining method. We also evaluated the generalizability of our method to similar tasks using two public datasets. The results demonstrate that our approach outperforms the current state-of-the-art (SOTA) self-supervised representation learning methods in various surgical image segmentation tasks.

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to inferior generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to effectively integrate surgical-specific information with SAM’s pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes and eliminates the use of explicit prompts for improved robustness and a simpler pipeline. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning, further enhancing the discrimination of the class prototypes for more accurate class prompting. The results of extensive experiments on both EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves state-of-the-art performance while only requiring a small number of tunable parameters. The source code is available at https://github.com/wenxi-yue/SurgicalSAM.

A lightweight segmentation network for endoscopic surgical instruments based on edge refinement and efficient self-attention.

In robot-assisted surgical systems, surgical instrument segmentation is a critical task that provides important information for surgeons to make informed decisions and ensure surgical safety. However, current mainstream models often lack precise segmentation edges and suffer from an excess of parameters, rendering their deployment challenging. To address these issues, this article proposes a lightweight semantic segmentation model based on edge refinement and efficient self-attention. The proposed model utilizes a lightweight densely connected network for feature extraction, which is able to extract high-quality semantic information with fewer parameters. The decoder combines a feature pyramid module with an efficient criss-cross self-attention module. This fusion integrates multi-scale data, strengthens focus on surgical instrument details, and enhances edge segmentation accuracy. To train and evaluate the proposed model, the authors developed a private dataset of endoscopic surgical instruments. It containing 1,406 images for training, 469 images for validation and 469 images for testing. The proposed model performs well on this dataset with only 466 K parameters, achieving a mean Intersection over Union (mIoU) of 97.11%. In addition, the model was trained on public datasets Kvasir-instrument and Endovis2017. Excellent results of 93.24% and 95.83% were achieved on the indicator mIoU, respectively. The superiority and effectiveness of the method are proved. Experimental results show that the proposed model has lower parameters and higher accuracy than other state-of-the-art models. The proposed model thus lays the foundation for further research in the field of surgical instrument segmentation.

MFF-Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments.

In robot-assisted surgery, automatic segmentation of surgical instrument images is crucial for surgical safety. The proposed method addresses challenges in the craniotomy environment, such as occlusion and illumination, through an efficient surgical instrument segmentation network. The network uses YOLOv8 as the target detection framework and integrates a semantic segmentation head to achieve detection and segmentation capabilities. A concatenation of multi-channel feature maps is designed to enhance model generalisation by fusing deep and shallow features. The innovative GBC2f module ensures the lightweight of the network and the ability to capture global information. Experimental validation of the intracranial glioma surgical instrument dataset shows excellent performance: 94.9% MPA score, 89.9% MIoU value, and 126.6 FPS. According to the experimental results, the segmentation model proposed in this study has significant advantages over other state-of-the-art models. This provides a valuable reference for the further development of intelligent surgical robots.

SINet: A hybrid deep CNN model for real-time detection and segmentation of surgical instruments

ObjectiveDetection and segmentation of surgical instruments is an indispensable technology in robot-assisted surgery that enables doctors to obtain more comprehensive visual information and further improve the safety of surgery. However, the results of a detection are more easily interfered by environmental factors, such as instrument shaking, incomplete displays and insufficient light. To overcome those issues, we designed a hybrid deep-CNN model (SINet) for real-time surgical instrument detection and segmentation. MethodsThe framework employs YOLOv5 as the object detection model and introduces a GAM attention mechanism to improve its feature extraction abilities. During training, the SiLU activation function is adopted to avoid gradient explosions and unstable training situations. Specifically, the vector angle relationship between the ground truth boxes and the prediction boxes was applied in the SIoU loss function to reduce the degree of freedom of the regression and accelerate the network convergence. Finally, a semantic segmentation head is used to implement detections of the surgical instruments by paralleling the detection and segmentation. ResultsThe proposed method is evaluated on the m2cai16-tool-locations public dataset and achieved a significant 97.9% mean average precision (mAP), 133 frames per second (FPS), 85.7% mean intersection over union (MIoU) and 86.6% Dice. Experiment based on simulated surgery platform also shows satisfactory detection performance. ConclusionExperimental results demonstrated that the SINet can effectively detect the pose of surgical instruments and achieves a better performance than most of the current algorithms. The method has the potential to help perform a series of surgical operations efficiently and safely.

Branch Aggregation Attention Network for Robotic Surgical Instrument Segmentation.

Surgical instrument segmentation is of great significance to robot-assisted surgery, but the noise caused by reflection, water mist, and motion blur during the surgery as well as the different forms of surgical instruments would greatly increase the difficulty of precise segmentation. A novel method called Branch Aggregation Attention network (BAANet) is proposed to address these challenges, which adopts a lightweight encoder and two designed modules, named Branch Balance Aggregation module (BBA) and Block Attention Fusion module (BAF), for efficient feature localization and denoising. By introducing the unique BBA module, features from multiple branches are balanced and optimized through a combination of addition and multiplication to complement strengths and effectively suppress noise. Furthermore, to fully integrate the contextual information and capture the region of interest, the BAF module is proposed in the decoder, which receives adjacent feature maps from the BBA module and localizes the surgical instruments from both global and local perspectives by utilizing a dual branch attention mechanism. According to the experimental results, the proposed method has the advantage of being lightweight while outperforming the second-best method by 4.03%, 1.53%, and 1.34% in mIoU scores on three challenging surgical instrument datasets, respectively, compared to the existing state-of-the-art methods. Code is available at https://github.com/SWT-1014/BAANet.

InstrumentNet: An integrated model for real-time segmentation of intracranial surgical instruments

In robot-assisted surgery, precise surgical instrument segmentation technology can provide accurate location and pose data for surgeons, helping them perform a series of surgical operations efficiently and safely. However, there are still some interfering factors, such as surgical instruments being covered by tissue, multiple surgical instruments interlacing with each other, and instrument shaking during surgery. To better address these issues, an effective surgical instrument segmentation network called InstrumentNet is proposed, which adopts YOLOv7 as the object detection framework to achieve a real-time detection solution. Specifically, a multiscale feature fusion network is constructed, which aims to avoid problems such as feature redundancy and feature loss and enhance the generalization ability. Furthermore, an adaptive feature-weighted fusion mechanism is introduced to regulate network learning and convergence. Finally, a semantic segmentation head is introduced to integrate the detection and segmentation functions, and a multitask learning loss function is specifically designed to optimize the surgical instrument segmentation performance. The proposed segmentation model is validated on a dataset of intracranial surgical instruments provided by seven experts from Beijing Tiantan Hospital and achieved an mAP score of 93.5 %, Dice score of 82.49 %, and MIoU score of 85.48 %, demonstrating its universality and superiority. The experimental results demonstrate that the proposed model achieves good segmentation performance on surgical instruments compared to other advanced models and can provide a reference for developing intelligent medical robots.

CFFR-Net: A channel-wise features fusion and recalibration network for surgical instruments segmentation

Surgical instrument segmentation plays a crucial role in robot-assisted surgery by furnishing essential information about instrument location and orientation. This information not only enhances surgical planning but also augments the precision and safety of procedures. Despite promising strides in recent research on surgical instrument segmentation, accuracy still faces obstacles due to local feature processing limitations, surgical environment complexity, and instrument morphological variability. To address these challenges, we introduced the channel-wise features fusion and recalibration network (CFFR-Net). This network utilizes a dual-stream mechanism, combining a context-guided block and dense block for feature extraction. The context-guided block captures a variety of contextual information by using different dilation rates. Additionally, CFFR-Net employs a fusion mechanism that harmonizes context-guided and dense streams. This integration, along with the inclusion of Squeeze-and-Excitation attention, enhances both the precision and robustness of semantic instrument segmentation.We performed experiments using two publicly available datasets for surgical instrument segmentation: the Kvasir-instrument and Endovis2017 datasets. The results of these experiments were highly encouraging, as our proposed model exhibited remarkable performance on both datasets compared to the state-of-the-art methods. On the Kvasir-instrument set, our model achieved a Dice score of 95.84% and mean intersection over union (mIOU) value of 92.40%. Similarly, on the Endovis2017 set, it obtained a Dice score of 95.47% and mIOU value of 93.02%.

Multi-scale feature retention and aggregation for colorectal cancer diagnosis using gastrointestinal images

Colonoscopy is considered the gold standard for colorectal cancer diagnosis and prognosis. However, existing methods are less accurate and prone to overlooking lesions during gastrointestinal endoscopic examinations. Computer-assisted diagnosis combined with robot-assisted minimally invasive surgery (RMIS) can significantly help medical practitioners detect and treat lesions. Therefore, two novel architectures are developed for polyp and surgical instrument segmentation to aid colorectal cancer diagnosis, assessment, and treatment. Colorectal cancer segmentation network (CCS-Net) is the base network used in this study. It uses the maximum convolutional layers near the input image for effective feature extraction from low-level information. In addition, CCS-Net uses an efficient feature upsampling unit to efficiently increase the input spatial features’ map size. Hence, CCS-Net is capable of providing a fair performance with satisfactory computational efficiency The multi-scale feature retention and aggregation network (MFRA-Net) is the final network in this study. MFRA-Net is developed to improve the segmentation accuracy of the CCS-Net further as it uses multi-scale feature retention to retain low-level spatial features and transfers them to deep stages of the network. MFRA-Net also combines multi-scale high-strided low-level information with high-level information to boost network segmentation performance. Finally, all the transferred multi-scale features from the early stages of the network are aggregated with high-level features in the deep levels of the network. This multi-scale feature retention and aggregation mechanism enables the network to maintain a better segmentation performance compared with other methods even with challenging blur, specular reflection, low contrast, and high variation cases. We evaluated both architectures on four challenging datasets: Kvasir-SEG, CVC-ClinicDB, Kvasir-Instrument, and the UW-Sinus-Surgery-Live dataset. The proposed method achieves dice similarity coefficients of 95.98%, 94.19%, 92.81%, and 88.57% for the CVC-ClinicDB, Kvasir-SEG, Kvasir-Instrument, and UW-Sinus-Surgery-Live datasets. The proposed method achieves superior segmentation performance compared with state-of-the-art methods and requires only 4.9 million trainable parameters for complete training. Therefore, the proposed networks can effectively assist health professionals in surgical procedures and colorectal cancer diagnosis through surgical instruments and polyp segmentation, respectively.

CGBA-Net: context-guided bidirectional attention network for surgical instrument segmentation.

Automatic surgical instrument segmentation is a crucial step for robotic-aided surgery. Encoder-decoder construction-based methods often directly fuse high-level and low-level features by skip connection to supplement some detailed information. However, irrelevant information fusion also increases misclassification or wrong segmentation, especially for complex surgical scenes. Uneven illumination always results in instruments similar to other tissues of background, which greatly increases the difficulty of automatic surgical instrument segmentation. The paper proposes a novel network to solve the problem. The paper proposes to guide the network to select effective features for instrument segmentation. The network is named context-guided bidirectional attention network (CGBANet). The guidance connection attention (GCA) module is inserted into the network to adaptively filter out irrelevant low-level features. Moreover, we propose bidirectional attention (BA) module for the GCA module to capture both local information and local-global dependency for surgical scenes to provide accurate instrument features. The superiority of our CGBA-Net is verified by multiple instrument segmentation on two publicly available datasets of different surgical scenarios, including an endoscopic vision dataset (EndoVis 2018) and a cataract surgery dataset. Extensive experimental results demonstrate our CGBA-Net outperforms the state-of-the-art methods on two datasets. Ablation study based on the datasets proves the effectiveness of our modules. The proposed CGBA-Net increased the accuracy of multiple instruments segmentation, which accurately classifies and segments the instruments. The proposed modules effectively provided instrument-related features for the network.

TMA-Net: A Transformer-Based Multi-Scale Attention Network for Surgical Instrument Segmentation

The ability to accurately and automatically segment surgical instruments is one of the important prerequisites for reasonable and stable operation of surgical robots. The utilization of deep learning in medical image segmentation has gained widespread recognition in recent years, leading to the proposition of multiple network models designed for the segmentation of diverse medical images, among which the most effective one is U-Net and its variants. Nevertheless, these existing networks also have various drawbacks, such as limited contextual representation capability, insufficient local feature processing, etc. In order to solve the above problems so that more accurate surgical instrument segmentation performance can be obtained, a transformer-based multi-scale attention network is proposed, referred to as TMA-Net, for surgical instrument segmentation from endoscopic images to serve robot-assisted surgery. To enable more accurate extraction of image features, a dual-branch encoder structure is proposed to obtain stronger contexts. Further, to address the problem that the simple skip connection is insufficient for local feature processing, an attention feature fusion (AFF) module and an additive attention and concatenation (AAC) module are proposed for effective feature learning to filter out the irrelevant information in the low-level features. Furthermore, a multi-scale context fusion (MCF) block is introduced to enhance the local feature maps and capture multi-scale contextual information. The efficacy of proposed TMA-Net is demonstrated through experimentation on publicly available surgical instrument segmentation datasets, including Endovis2017 and UW-Sinus-Surgery-C/L. The results show that proposed TMA-Net outperforms existing methods in terms of surgical instrument segmentation accuracy.

MAF-Net: A multi-scale attention fusion network for automatic surgical instrument segmentation

Accurate localization of surgical instruments is of utmost importance for precise robot-assisted surgeries. With the development of artificial intelligence, deep convolutional neural networks (DCNNs) have been widely employed for automatic image segmentation, owing to their strong ability to generate contextual features, especially in the encoder–decoder framework. However, existing segmentation networks lack the feature capturing capability on micro objects and have shortcomings in processing local semantic features. These limitations can affect the precise segmentation of surgical instruments. In response to these issues, this paper proposes a multi-scale attention fusion network called MAF-Net, which comprises residual dense module, a multi-scale atrous convolution (MSAC) module, and an attention fusion module (AFM). To improve the processing ability of local contextual features, we propose replacing skip connections with residual dense module to acquire stronger contexts. Furthermore, a MSAC module is proposed for local feature enhancement, thereby enhancing attention on multi-scale features. In addition, an AFM module is introduced to integrate multi-scale information by cross-scale feature fusion. Experimental results, using two public datasets, Endovis2017 and kvasir-instrument, demonstrate that the proposed network has the ability to achieve precise surgical instrument segmentation and outperforms related advanced methods.

FUN-SIS: A Fully UNsupervised approach for Surgical Instrument Segmentation.

Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensive to collect at large scale. In this paper, we present FUN-SIS, a Fully-UNsupervised approach for binary Surgical Instrument Segmentation. FUN-SIS trains a per-frame segmentation model on completely unlabelled endoscopic videos, by solely relying on implicit motion information and instrument shape-priors. We define shape-priors as realistic segmentation masks of the instruments, not necessarily coming from the same dataset/domain as the videos. The shape-priors can be collected in various and convenient ways, such as recycling existing annotations from other datasets. We leverage them as part of a novel generative-adversarial approach, allowing to perform unsupervised instrument segmentation of optical-flow images during training. We then use the obtained instrument masks as pseudo-labels in order to train a per-frame segmentation model; to this aim, we develop a learning-from-noisy-labels architecture, designed to extract a clean supervision signal from these pseudo-labels, leveraging their peculiar noise properties. We validate the proposed contributions on three surgical datasets, including the MICCAI 2017 EndoVis Robotic Instrument Segmentation Challenge dataset. The obtained fully-unsupervised results for surgical instrument segmentation are almost on par with the ones of fully-supervised state-of-the-art approaches. This suggests the tremendous potential of the proposed method to leverage the great amount of unlabelled data produced in the context of minimally invasive surgery.

Unpaired deep adversarial learning for multi-class segmentation of instruments in robot-assisted surgical videos.

Image segmentation of instruments in the raw surgical videos is a critical component of intraoperative assistance softwares. Challenges include addressing rendered overlays occluding the instrument while providing pivotal input to instrument tracking frameworks and, train the segmentation process with limited labelled data available from surgical videos. The proposed adversarial network, InstruSegNet uses unpaired training (eliminating need for massive paired data) for automated multi-class surgical instrument segmentation in raw surgical videos with complex backgrounds. The proposed method is applied for single/multiple robotic and rigid instruments and optimised on least square Generative Adversarial Networks loss. Promising validation has been conducted on the publicly available dataset. Proposed approach for multi-class segmentation of robotic and rigid instruments meets outstanding performance in terms of accuracy and surpasses the existing methods. This work facilitates segmenting instrument information without manual interventions from raw videos providing means to code surgeon's actions for developing intelligent assistance software.

Surgical smoke removal via residual Swin transformer network.

In robot-assisted minimally invasive surgery (RMIS), smoke produced by laser ablation and cauterization causes degradation in the visual quality of the operating field, increasing the difficulty and risk of surgery. Therefore, it is important and meaningful to remove fog or smoke from the endoscopic video to maintain a clear visual field. In this paper, we propose a novel method for surgical smoke removal based on the Swin transformer. Our method firstly uses convolutional neural network to extract shallow features, then uses the Swin transformer block to further extract deep features and finally generates smoke-free images. We conduct quantitative and qualitative experiments on the proposed method, and we also validate the desmoking results in the surgical instrument segmentation task. Extensive experiments on synthetic and real dataset show that the proposed approach has good performance and outperforms the state-of-the-art surgical smoke removal methods. Our method effectively removes surgical smoke, improves image quality and reduces the risk of RMIS. It provides a clearer visual field for the surgeon, as well as for subsequent visual tasks, such as instrument segmentation, 3D scene reconstruction and surgery automation.

TMF-Net: A Transformer-Based Multiscale Fusion Network for Surgical Instrument Segmentation From Endoscopic Images

Automatic surgical instrument segmentation is a necessary step for the steady operation of surgical robots, and the segmentation accuracy directly affects the surgical effect. Nevertheless, accurate surgical instrument segmentation from endoscopic images remains a challenging task due to the complex environment and instrument motion during surgery. Based on the encoder–decoder structure, a transformer-based multiscale fusion network, named TMF-Net, is proposed to address the difficulties in the area of surgical instrument segmentation. To realize the effective feature representation based on the pretrained ResNet34 and transformer and to strengthen both advantages of different encoder units, a dual-encoder unit is proposed to simultaneously learn the semantic relationship between adjacent pixels and distant pixels and comprehensively capture the global context information. Meanwhile, to retain more contextual information, a trapezoid atrous spatial pyramid pooling (trapezoid ASPP) block is proposed for feature enhancement of local features with different receptive fields to enrich feature information. Furthermore, in addition to multiscale surgical instruments in endoscopic images, a multiscale attention fusion (MAF) block is proposed to fuse multiscale feature maps to make the segmentation network direct more attention to the efficient channels so that it can improve the segmentation accuracy. Two typical datasets are used for performance analysis and verification, including Kvasir-Instrument and Endovis2017. Experimental results indicate that the proposed TMF-Net could effectively improve segmentation accuracy on surgical instruments, and it could also yield a competitive segmentation result in comparison with advanced detection methods.

MSDE-Net: A Multi-Scale Dual-Encoding Network for Surgical Instrument Segmentation.

Minimally invasive surgery, which relies on surgical robots and microscopes, demands precise image segmentation to ensure safe and efficient procedures. Nevertheless, achieving accurate segmentation of surgical instruments remains challenging due to the complexity of the surgical environment. To tackle this issue, this paper introduces a novel multiscale dual-encoding segmentation network, termed MSDE-Net, designed to automatically and precisely segment surgical instruments. The proposed MSDE-Net leverages a dual-branch encoder comprising a convolutional neural network (CNN) branch and a transformer branch to effectively extract both local and global features. Moreover, an attention fusion block (AFB) is introduced to ensure effective information complementarity between the dual-branch encoding paths. Additionally, a multilayer context fusion block (MCF) is proposed to enhance the network's capacity to simultaneously extract global and local features. Finally, to extend the scope of global feature information under larger receptive fields, a multi-receptive field fusion (MRF) block is incorporated. Through comprehensive experimental evaluations on two publicly available datasets for surgical instrument segmentation, the proposed MSDE-Net demonstrates superior performance compared to existing methods.

A dual-encoder feature attention network for surgical instrument segmentation

A dual-encoder feature attention network for surgical instrument segmentation

SSIS-Seg: Simulation-Supervised Image Synthesis for Surgical Instrument Segmentation.

Surgical instrument segmentation can be used in a range of computer assisted interventions and automation in surgical robotics. While deep learning architectures have rapidly advanced the robustness and performance of segmentation models, most are still reliant on supervision and large quantities of labelled data. In this paper, we present a novel method for surgical image generation that can fuse robotic instrument simulation and recent domain adaptation techniques to synthesize artificial surgical images to train surgical instrument segmentation models. We integrate attention modules into well established image generation pipelines and propose a novel cost function to support supervision from simulation frames in model training. We provide an extensive evaluation of our method in terms of segmentation performance along with a validation study on image quality using evaluation metrics. Additionally, we release a novel segmentation dataset from real surgeries that will be shared for research purposes. Both binary and semantic segmentation have been considered, and we show the capability of our synthetic images to train segmentation models compared with the latest methods from the literature.