Automated image-based analysis of spatio-temporal fungal dynamics.

Due to their ability to grow in complex environments, fungi play an important role in most ecosystems and have for that reason been the subject of numerous studies. Some of the main obstacles to the study of fungal growth are the heterogeneity of growth environments and the limited scope of laboratory experiments. Given the increasing availability of image capturing techniques, a new approach lies in image analysis. Most previous image analysis studies involve manual labelling of the fungal network, tracking of individual hyphae, or invasive techniques that do not allow for tracking the evolution of the entire fungal network. In response, this work presents a highly versatile tool combining image analysis and graph theory to monitor fungal growth through time and space for different fungal species and image resolutions. In addition, a new experimental set-up is presented that allows for a functional description of fungal growth dynamics and a quantitative mutual comparison of different growth behaviors. The presented method is completely automated and facilitates the extraction of the most studied fungal growth features such as the total length of the mycelium, the area of the mycelium and the fractal dimension. The compactness of the fungal network can also be monitored over time by computing measures such as the number of tips, the node degree and the number of nodes. Finally, the average growth angle and the internodal length can be extracted to study the morphology of the fungi. In summary, the introduced method offers an updated and broader alternative to classical and narrowly focused approaches, thus opening new avenues of investigation in the field of mycology.

Evaluating the impact of the learning curve on the perioperative outcomes of robot-assisted radical cystectomy with intracorporeal urinary diversion.

INTRODUCTION: Robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) is a technically difficult procedure. Our aim was to evaluate the potential impact of the learning curve (LC) on perioperative and pathological outcomes of RARC with ICUD. MATERIAL AND METHODS: Retrospective study of 62 consecutive patients who underwent RARC with ICUD for bladder cancer between 2015-2020. We compared 3 consecutive groups of 20 (G1), 20 (G2), and 22 (G3) patients to analyze the impact of the LC. G1 cases were performed by a senior surgeon experienced in robotic surgery, while G2-G3 were performed by 2 junior surgeons without experience under the mentorship of the senior surgeon. RESULTS: The 3 groups had similar clinical and pathological characteristics. A total of 15 patients (24%) received a neobladder and 47 (75%) an ileal conduit. The mean operative time decreased 60 min between G1-G3 (p = 0.001). No conversions to open approach or intraoperative complications were reported. There were no differences between groups regarding positive margin rates (p = 0.6) or the number of lymph nodes removed (p = 0.061). The postoperative complication rate was 77% and did not change during the LC (p = 0.49). Uretero-enteric stricture rate decreased from 25% in G1 to 9% in G3 (p = 0.217). CONCLUSIONS: The inclusion of junior surgeons to a RARC with ICUD program after the initial 20 cases does not have an impact on the perioperative and pathological outcomes of the procedure. The operative time and the uretero-enteric stricture rate could be reduced during the LC.

Evaluating the impact of the learning curve on the perioperative outcomes of robot-assisted radical cystectomy with intracorporeal urinary diversion. / Análisis del impacto de la curva de aprendizaje de la cistectomía radical asistida por robot con derivación urinaria intracorpórea sobre los resultados perioperatorios.

INTRODUCTION: Robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) is a technically difficult procedure. Our aim was to evaluate the potential impact of the learning curve (LC) on perioperative and pathological outcomes of RARC with ICUD. MATERIAL AND METHODS: Retrospective study of 62 consecutive patients who underwent RARC with ICUD for bladder cancer between 2015-2020. We compared 3 consecutive groups of 20 (G1), 20 (G2), and 22 (G3) patients to analyze the impact of the LC. G1 cases were performed by a senior surgeon experienced in robotic surgery, while G2-G3 were performed by 2 junior surgeons without experience under the mentorship of the senior surgeon. RESULTS: The 3 groups had similar clinical and pathological characteristics. A total of 15 patients (24%) received a neobladder and 47 (75%) an ileal conduit. The mean operative time decreased 60minutes between G1-G3 (P=0.001). No conversions to open approach or intraoperative complications were reported. There were no differences between groups regarding positive margin rates (P=0.6) or the number of lymph nodes removed (P=0.061). The postoperative complication rate was 77% and did not change during the LC (P=0.49). Uretero-enteric stricture rate decreased from 25% in G1 to 9% in G3 (P=0.217). CONCLUSIONS: The inclusion of júnior surgeons to a RARC with ICUD program after the initial 20 cases does not have an impact on the perioperative and pathological outcomes of the procedure. The operative time and the uretero-enteric stricture rate could be reduced during the LC.

Análisis del impacto de la curva de aprendizaje de la cistectomía radical asistida por robot con derivación urinaria intracorpórea sobre los resultados perioperatorios / Evaluating the impact of the learning curve on the perioperative outcomes of robot-assisted radical cystectomy with intracorporeal urinary diversion

Introducción La cistectomía radical asistida por robot (CRAR) con derivación urinaria intracorpórea (DUIC) es un procedimiento técnicamente complejo. Nuestro objetivo fue analizar el impacto de la curva de aprendizaje (CA) de la CRAR con DUIC sobre los resultados perioperatorios y patológicos.Material y métodos Estudio retrospectivo de 62 pacientes consecutivos intervenidos mediante CRAR con DUIC por tumor vesical entre 2015 y 2020. Se compararon 3 grupos consecutivos de 20 (G1), 20 (G2) y 22 (G3) pacientes para analizar el impacto de la CA. Los casos de G1 fueron intervenidos por un cirujano sénior con experiencia en cirugía robótica y los de G2-G3 por 2cirujanos júnior sin experiencia, pero tutorizados por el sénior.Resultados Los 3grupos tenían características clínico-patológicas similares. A 15 pacientes (24%) se les realizó una neovejiga y a 47 (75%) un conducto ileal. El tiempo medio operatorio descendió 60 min entre G1 y G3 (p=0,001). Ningún paciente precisó conversión a cirugía abierta ni tuvo complicaciones intraoperatorias. No se objetivaron diferencias en la tasa de márgenes positivos (p=0,6) ni en el número de ganglios extraídos (p=0,061) entre los grupos. La tasa de complicaciones postoperatorias fue del 77% y no varió durante la CA (p=0,49). Se objetivó una tendencia en la reducción de tasa de estenosis ureteroileal del 25% en G1 al 9% en G3 (p=0,217).Conclusiones La incorporación de cirujanos júnior a un programa de CRAR con DUIC a partir de los 20 primeros casos no compromete los resultados perioperatorios ni patológicos. Durante la CA se podría reducir el tiempo operatorio y la tasa de estenosis ureteroileal (AU)

Introduction Robot-assisted radical cystectomy (RARC) with intracorporeal urinary diversion (ICUD) is a technically difficult procedure. Our aim was to evaluate the potential impact of the learning curve (LC) on perioperative and pathological outcomes of RARC with ICUD.Material and methods Retrospective study of 62 consecutive patients who underwent RARC with ICUD for bladder cancer between 2015-2020. We compared 3 consecutive groups of 20 (G1), 20 (G2), and 22 (G3) patients to analyze the impact of the LC. G1 cases were performed by a senior surgeon experienced in robotic surgery, while G2-G3 were performed by 2 junior surgeons without experience under the mentorship of the senior surgeon.Results The 3 groups had similar clinical and pathological characteristics. A total of 15 patients (24%) received a neobladder and 47 (75%) an ileal conduit. The mean operative time decreased 60minutes between G1-G3 (P=0.001). No conversions to open approach or intraoperative complications were reported. There were no differences between groups regarding positive margin rates (P=0.6) or the number of lymph nodes removed (P=0.061). The postoperative complication rate was 77% and did not change during the LC (P=0.49). Uretero-enteric stricture rate decreased from 25% in G1 to 9% in G3 (P=0.217).Conclusions The inclusion of júnior surgeons to a RARC with ICUD program after the initial 20 cases does not have an impact on the perioperative and pathological outcomes of the procedure. The operative time and the uretero-enteric stricture rate could be reduced during the LC (AU)

Separability Criteria for the Evaluation of Boundary Detection Benchmarks.

There exist a significant number of benchmarks for evaluating the performance of boundary detection algorithms, most of them relying on some sort of comparison of the automatically-generated boundaries with human-labeled ones. Such benchmarks are composed of a representative image data set, as well as a comparison measure on the universe of boundary images. Despite many such data sets and measures have been proposed, there is no clear way of knowing which combinations of them are the most suitable for the task. In this paper, we introduce four criteria that allow for a sensible evaluation of the performance of a comparison measure on a given data set. The criteria mimic the way in which humans understand boundary images, as well as their ability to recognize the underlying scenes. These criteria can, as a final goal, quantify the ability of the boundary detection benchmarks to evaluate the performance of boundary detection methods, either edge-based or segmentation-based.