A cone-beam X-ray computed tomography data collection designed for machine learning

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation

Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy

OBJECTIVE: To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using deep learning. BACKGROUND: RAMIE is a complex operation with substantial perioperative morbidity and a considerable learning curve. Automatic anatomy recognition may improve surgical orientation and recognition of anatomical structures and might contribute to reducing morbidity or learning curves. Studies regarding anatomy recognition in complex surgical procedures are currently lacking. METHODS: Eighty-three videos of consecutive RAMIE procedures between 2018 and 2022 were retrospectively collected at University Medical Center Utrecht. A surgical PhD candidate and an expert surgeon annotated the azygos vein and vena cava, aorta, and right lung on 1050 thoracoscopic frames. 850 frames were used for training of a convolutional neural network (CNN) to segment the anatomical structures. The remaining 200 frames of the dataset were used for testing the CNN. The Dice and 95% Hausdorff distance (95HD) were calculated to assess algorithm accuracy. RESULTS: The median Dice of the algorithm was 0.79 (IQR = 0.20) for segmentation of the azygos vein and/or vena cava. A median Dice coefficient of 0.74 (IQR = 0.86) and 0.89 (IQR = 0.30) were obtained for segmentation of the aorta and lung, respectively. Inference time was 0.026 s (39 Hz). The prediction of the deep learning algorithm was compared with the expert surgeon annotations, showing an accuracy measured in median Dice of 0.70 (IQR = 0.19), 0.88 (IQR = 0.07), and 0.90 (0.10) for the vena cava and/or azygos vein, aorta, and lung, respectively. CONCLUSION: This study shows that deep learning-based semantic segmentation has potential for anatomy recognition in RAMIE video frames. The inference time of the algorithm facilitated real-time anatomy recognition. Clinical applicability should be assessed in prospective clinical studies

Teams between Neo-Taylorism and Anti-Taylorism

The concept of teamworking is the product of two distinct developments. One: a neo- Tayloristic form of organization of work, of which Toyota has shown that it can be very profitable, was packaged and reframed to make it acceptable to the Western public. Two: anti-Tayloristic ways of organizing work, inspired by ideals of organizational democracy, were relabeled to make these acceptable to profit-oriented managers. Drawing on empirical research in Scandinavia, Germany, The Netherlands and the UK, as well as on published case studies of Japanese companies, the paper develops a neo-Tayloristic and an anti-Tayloristic model of teamworking. Key concerns in the teamworking literature are intensification of work and the use of shop floor autonomy as a cosmetic or manipulative device. Indeed, all the features of neo-Tayloristic teamworking are geared towards the intensification of work. However, one of the intensification mechanisms, the removal of Tayloristic rigidities in the division of labor, applies to anti-Tayloristic teamworking as well. This poses a dilemma for employee representatives. In terms of autonomy, on the other hand, the difference between neo-Tayloristic and anti-Tayloristic teamworking is real. In anti-Tayloristic teamworking, there is no supervisor inside the team. The function of spokesperson rotates. All team members can participate in decision-making. Standardization is not relentlessly pursued; management accepts some measure of worker control. There is a tendency to alleviate technical discipline, e.g. to find alternatives for the assembly line. Buffers are used. Remuneration is based on proven skill level; there are no group bonuses. In contrast, in neo-Tayloristic teamworking, a permanent supervisor is present in the team as team leader. At most, only the team leader can participate in decision-making. Standardization is relentlessly pursued. Management prerogatives are nearly unlimited. Job designers treat technical discipline, e.g. short-cycled work on the assembly line, as unproblematic. There are no buffers. A substantial part of wages consists of individual bonuses based on assessments by supervisors on how deeply workers cooperate in the system. Group bonuses are also given. The instability and vulnerability of anti-Tayloristic teamworking imply that it can only develop and flourish when managers and employee representatives put determined effort into it. The opportunity structure for this contains both economic and political elements. In mass production, the economic success of Toyota, through skillful mediation by management gurus, makes the opportunity structure for anti-Tayloristic teamworking relatively unfavorable

Improvement and Transfer of Practice-directed Knowledge

action research, action science, evaluation of interventions, intended effects, dissemination of practice-directed knowledge,

Comparison of convolutional neural network training strategies for cone-beam CT image segmentation

© 2021Background and objective: Over the past decade, convolutional neural networks (CNNs) have revolutionized the field of medical image segmentation. Prompted by the developments in computational resources and the availability of large datasets, a wide variety of different two-dimensional (2D) and three-dimensional (3D) CNN training strategies have been proposed. However, a systematic comparison of the impact of these strategies on the image segmentation performance is still lacking. Therefore, this study aimed to compare eight different CNN training strategies, namely 2D (axial, sagittal and coronal slices), 2.5D (3 and 5 adjacent slices), majority voting, randomly oriented 2D cross-sections and 3D patches. Methods: These eight strategies were used to train a U-Net and an MS-D network for the segmentation of simulated cone-beam computed tomography (CBCT) images comprising randomly-placed non-overlapping cylinders and experimental CBCT images of anthropomorphic phantom heads. The resulting segmentation performances were quantitatively compared by calculating Dice similarity coefficients. In addition, all segmented and gold standard experimental CBCT images were converted into virtual 3D models and compared using orientation-based surface comparisons. Results: The CNN training strategy that generally resulted in the best performances on both simulated and experimental CBCT images was majority voting. When employing 2D training strategies, the segmentation performance can be optimized by training on image slices that are perpendicular to the predominant orientation of the anatomical structure of interest. Such spatial features should be taken into account when choosing or developing novel CNN training strategies for medical image segmentation. Conclusions: The results of this study will help clinicians and engineers to choose the most-suited CNN training strategy for CBCT image segmentation