Laparoscopy is an imaging technique that enables minimally-invasive procedures in various medical disciplines including abdominal surgery, gynaecology and urology. To date, publicly available laparoscopic image datasets are mostly limited to general classifications of data, semantic segmentations of surgical instruments and low-volume weak annotations of specific abdominal organs. The Dresden Surgical Anatomy Dataset provides semantic segmentations of eight abdominal organs (colon, liver, pancreas, small intestine, spleen, stomach, ureter, vesicular glands), the abdominal wall and two vessel structures (inferior mesenteric artery, intestinal veins) in laparoscopic view. In total, this dataset comprises 13195 laparoscopic images. For each anatomical structure, we provide over a thousand images with pixel-wise segmentations. Annotations comprise semantic segmentations of single organs and one multi-organ-segmentation dataset including segments for all eleven anatomical structures. Moreover, we provide weak annotations of organ presence for every single image. This dataset markedly expands the horizon for surgical data science applications of computer vision in laparoscopic surgery and could thereby contribute to a reduction of risks and faster translation of Artificial Intelligence into surgical practice.

Background: Complex oncological procedures pose various surgical challenges including dissection in distinct tissue planes and preservation of vulnerable anatomical structures throughout different surgical phases. In rectal surgery, a violation of dissection planes increases the risk of local recurrence and autonomous nerve damage resulting in incontinence and sexual dysfunction. While deep learning-based identification of target structures has been described in basic laparoscopic procedures, feasibility of artificial intelligence-based guidance has not yet been investigated in complex abdominal surgery. Methods: A dataset of 57 robot-assisted rectal resection (RARR) videos was split into a pre-training dataset of 24 temporally non-annotated videos and a training dataset of 33 temporally annotated videos. Based on phase annotations and pixel-wise annotations of randomly selected image frames, convolutional neural networks were trained to distinguish surgical phases and phase-specifically segment anatomical structures and tissue planes. To evaluate model performance, F1 score, Intersection-over-Union (IoU), precision, recall, and specificity were determined. Results: We demonstrate that both temporal (average F1 score for surgical phase recognition: 0.78) and spatial features of complex surgeries can be identified using machine learning-based image analysis. Based on analysis of a total of 8797 images with pixel-wise target structure segmentations, mean IoUs for segmentation of anatomical target structures range from 0.09 to 0.82 and from 0.05 to 0.32 for dissection planes and dissection lines throughout different phases of RARR in our analysis. Conclusions: Image-based recognition is a promising technique for surgical guidance in complex surgical procedures. Future research should investigate clinical applicability, usability, and therapeutic impact of a respective guidance system.

Background: Lack of anatomy recognition represents a clinically relevant risk factor in abdominal surgery. While machine learning methods have the potential to aid in recognition of visible patterns and structures, limited availability and diversity of (annotated) laparoscopic image data restrict the clinical potential of such applications in practice. This study explores the potential of machine learning algorithms to identify and delineate abdominal organs and anatomical structures using a robust and comprehensive dataset, and compares algorithm performance to that of humans. Methods: Based on the Dresden Surgical Anatomy Dataset providing 13195 laparoscopic images with pixel-wise segmentations of eleven anatomical structures, two machine learning algorithms were developed: individual segmentation algorithms for each structure, and a combined algorithm with a common encoder and structure-specific decoders. Performance was assessed using F1 score, Intersection-over-Union (IoU), precision, recall, and specificity. Using the example of pancreas segmentation on a sample dataset of 35 images, algorithm performance was compared to that of a cohort of 28 physicians, medical students, and medical laypersons. Results: Mean IoU for segmentation of intraabdominal structures ranged from 0.28 to 0.83 and from 0.32 to 0.81 for the structure-specific and the combined semantic segmentation model, respectively. Average inference for the structure-specific (one anatomical structure) and the combined model (eleven anatomical structures) took 20 ms and 54 ms, respectively. The structure-specific model performed equal to or better than 27 out of 28 human participants in pancreas segmentation. Conclusions: Machine learning 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.

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.