Laparoscopic Pancreas Surgery: Image Guidance Solutions

Pancreatic ductal adenocarcinoma (PDA) is the fourth leading cause of cancer-related deaths. Surgery is the only viable treatment, but irradical resection rates are still high. Laparoscopic pancreatic surgery has some technical limitations for surgeons and tumor identification may be challenging. Image-guided techniques provide intraoperative margin assessment and visualization methods, which may be advantageous in guiding the surgeon to achieve curative resections and therefore improve the surgical outcomes. In this chapter, current available laparoscopic surgical approaches and image-guided techniques for pancreatic surgery are reviewed. Surgical outcomes of pancreaticoduodenectomy and distal pancreatectomy performed by laparoscopy, laparoendoscopic single-site surgery (LESS), and robotic surgery are included and analyzed. Besides, image-guided techniques such as intraoperative near-infrared fluorescence imaging and surgical navigation are presented as emerging techniques. Results show that minimally invasive procedures reported a reduction of blood loss, reduced length of hospital stay, and positive resection margins, as well as an improvement in spleen-preserving rates, when compared to open surgery. Studies reported that fluorescence-guided pancreatic surgery might be beneficial in cases where the pancreatic anatomy is difficult to identify. The first approach of a surgical navigation system for guidance during pancreatic resection procedures is presented, combining preoperative images (CT and MRI) with intraoperative laparoscopic ultrasound imaging

Unravelling the effect of data augmentation transformations in polyp segmentation

Purpose: Data augmentation is a common technique to overcome the lack of large annotated databases, a usual situation when applying deep learning to medical imaging problems. Nevertheless, there is no consensus on which transformations to apply for a particular field. This work aims at identifying the effect of different transformations on polyp segmentation using deep learning. Methods: A set of transformations and ranges have been selected, considering image-based (width and height shift, rotation, shear, zooming, horizontal and vertical flip and elastic deformation), pixel-based (changes in brightness and contrast) and application-based (specular lights and blurry frames) transformations. A model has been trained under the same conditions without data augmentation transformations (baseline) and for each of the transformation and ranges, using CVC-EndoSceneStill and Kvasir-SEG, independently. Statistical analysis is performed to compare the baseline performance against results of each range of each transformation on the same test set for each dataset. Results: This basic method identifies the most adequate transformations for each dataset. For CVC-EndoSceneStill, changes in brightness and contrast significantly improve the model performance. On the contrary, Kvasir-SEG benefits to a greater extent from the image-based transformations, especially rotation and shear. Augmentation with synthetic specular lights also improves the performance. Conclusion: Despite being infrequently used, pixel-based transformations show a great potential to improve polyp segmentation in CVC-EndoSceneStill. On the other hand, image-based transformations are more suitable for Kvasir-SEG. Problem-based transformations behave similarly in both datasets. Polyp area, brightness and contrast of the dataset have an influence on these differences.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 research and innovation programme under Grant Agreement No 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold standard procedure for diagnosis and removal of colorectal lesions with potential to evolve into cancer and computer-aided detection systems can help gastroenterologists to increase the adenoma detection rate, one of the main indicators for colonoscopy quality and predictor for colorectal cancer prevention. The recent success of deep learning approaches in computer vision has also reached this field and has boosted the number of proposed methods for polyp detection, localization and segmentation. Through a systematic search, 35 works have been retrieved. The current systematic review provides an analysis of these methods, stating advantages and disadvantages for the different categories used; comments seven publicly available datasets of colonoscopy images; analyses the metrics used for reporting and identifies future challenges and recommendations. Convolutional neural networks are the most used architecture together with an important presence of data augmentation strategies, mainly based on image transformations and the use of patches. End-to-end methods are preferred over hybrid methods, with a rising tendency. As for detection and localization tasks, the most used metric for reporting is the recall, while Intersection over Union is highly used in segmentation. One of the major concerns is the difficulty for a fair comparison and reproducibility of methods. Even despite the organization of challenges, there is still a need for a common validation framework based on a large, annotated and publicly available database, which also includes the most convenient metrics to report results. Finally, it is also important to highlight that efforts should be focused in the future on proving the clinical value of the deep learning based methods, by increasing the adenoma detection rate.This work was partially supported by PICCOLO project. This project has received funding from the European Union's Horizon2020 Research and Innovation Programme under grant agreement No. 732111. The sole responsibility of this publication lies with the author. The European Union is not responsible for any use that may be made of the information contained therein. The authors would also like to thank Dr. Federico Soria for his support on this manuscript and Dr. José Carlos Marín, from Hospital 12 de Octubre, and Dr. Ángel Calderón and Dr. Francisco Polo, from Hospital de Basurto, for the images in Fig. 4

TELMA: Technology enhanced learning environment for minimally invasive surgery

Background: Cognitive skills training for minimally invasive surgery has traditionally relied upon diverse tools, such as seminars or lectures. Web technologies for e-learning have been adopted to provide ubiquitous training and serve as structured repositories for the vast amount of laparoscopic video sources available. However, these technologies fail to offer such features as formative and summative evaluation, guided learning, or collaborative interaction between users. Methodology: The "TELMA" environment is presented as a new technology-enhanced learning platform that increases the user's experience using a four-pillared architecture: (1) an authoring tool for the creation of didactic contents; (2) a learning content and knowledge management system that incorporates a modular and scalable system to capture, catalogue, search, and retrieve multimedia content; (3) an evaluation module that provides learning feedback to users; and (4) a professional network for collaborative learning between users. Face validation of the environment and the authoring tool are presented. Results: Face validation of TELMA reveals the positive perception of surgeons regarding the implementation of TELMA and their willingness to use it as a cognitive skills training tool. Preliminary validation data also reflect the importance of providing an easy-to-use, functional authoring tool to create didactic content. Conclusion: The TELMA environment is currently installed and used at the Jesús Usón Minimally Invasive Surgery Centre and several other Spanish hospitals. Face validation results ascertain the acceptance and usefulness of this new minimally invasive surgery training environment

Medical needs related to the endoscopic technology and colonoscopy for colorectal cancer diagnosis

Background. The high incidence and mortality rate of colorectal cancer require new technologies to improve its early diagnosis. This study aims at extracting the medical needs related to the endoscopic technology and the colonoscopy procedure currently used for colorectal cancer diagnosis, essential for designing these demanded technologies. Methods. Semi-structured interviews and an online survey were used. Results. Six endoscopists were interviewed and 103 were surveyed, obtaining the demanded needs that can be divided into: a) clinical needs, for better polyp detection and classification (especially flat polyps), location, size, margins and penetration depth; b) computer-aided diagnosis (CAD) system needs, for additional visual information supporting polyp characterization and diagnosis; and c) operational/physical needs, related to limitations of image quality, colon lighting, flexibility of the endoscope tip, and even poor bowel preparation.This work is part of the PICCOLO project, which has received funding from the European Union’s Horizon 2020 research and innovation Programme under grant agreement No. 732111. GR18199, funded by “Consejería de Economía, Ciencia y Agenda Digital, Junta de Extremadura” and co-funded by European Union (ERDF “A way to make Europe”). The funding bodies did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

Physiologic Responses to Infrarenal Aortic Cross-Clamping during Laparoscopic or Conventional Vascular Surgery in Experimental Animal Model: Comparative Study

The aim of this study was to compare the hemodynamic and ventilatory effects of prolonged infrarenal aortic cross-clamping in pigs undergoing either laparotomy or laparoscopy. 18 pigs were used for this study. Infrarenal aortic crossclamping was performed for 60 minutes in groups I (laparotomy, n = 6) and II (laparoscopy, n = 6). Group III (laparoscopy, n = 6) underwent a 120-minute long pneumoperitoneum in absence of aortic clamping (sham group). Ventilatory and hemodynamic parameters and renal function were serially determined in all groups. A significant decrease in pH and significant increase in PaCO2 were observed in group II, whereas no changes in these parameters were seen in group I and III. All variables returned to values similar to baseline in groups I and II 60 minutes after declamping. A significant increase in renal resistive index was evidenced during laparoscopy, with significantly higher values seen in Group II. Thus a synergic effect of pneumoperitoneum and aortic cross-clamping was seen in this study. These two factors together cause decreased renal perfusion and acidosis, thus negatively affecting the patient's general state during this type of surgery

PICCOLO White-Light and Narrow-Band Imaging Colonoscopic Dataset: A Performance Comparative of Models and Datasets

Colorectal cancer is one of the world leading death causes. Fortunately, an early diagnosis allows for e_ective treatment, increasing the survival rate. Deep learning techniques have shown their utility for increasing the adenoma detection rate at colonoscopy, but a dataset is usually required so the model can automatically learn features that characterize the polyps. In this work, we present the PICCOLO dataset, that comprises 3433 manually annotated images (2131 white-light images 1302 narrow-band images), originated from 76 lesions from 40 patients, which are distributed into training (2203), validation (897) and test (333) sets assuring patient independence between sets. Furthermore, clinical metadata are also provided for each lesion. Four di_erent models, obtained by combining two backbones and two encoder–decoder architectures, are trained with the PICCOLO dataset and other two publicly available datasets for comparison. Results are provided for the test set of each dataset. Models trained with the PICCOLO dataset have a better generalization capacity, as they perform more uniformly along test sets of all datasets, rather than obtaining the best results for its own test set. This dataset is available at the website of the Basque Biobank, so it is expected that it will contribute to the further development of deep learning methods for polyp detection, localisation and classification, which would eventually result in a better and earlier diagnosis of colorectal cancer, hence improving patient outcomes.This work was partially supported by PICCOLO project. This project has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 732111. Furthermore, this publication has also been partially supported by GR18199 from Consejería de Economía, Ciencia y Agenda Digital of Junta de Extremadura (co-funded by European Regional Development Fund–ERDF. “A way to make Europe”/ “Investing in your future”. This work has been performed by the ICTS “NANBIOSIS” at the Jesús Usón Minimally Invasive Surgery Centre

Systems and technologies for objective evaluation of technical skills in laparoscopic surgery

Minimally invasive surgery is a highly demanding surgical approach regarding technical requirements for the surgeon, who must be trained in order to perform a safe surgical intervention. Traditional surgical education in minimally invasive surgery is commonly based on subjective criteria to quantify and evaluate surgical abilities, which could be potentially unsafe for the patient. Authors, surgeons and associations are increasingly demanding the development of more objective assessment tools that can accredit surgeons as technically competent. This paper describes the state of the art in objective assessment methods of surgical skills. It gives an overview on assessment systems based on structured checklists and rating scales, surgical simulators, and instrument motion analysis. As a future work, an objective and automatic assessment method of surgical skills should be standardized as a means towards proficiency-based curricula for training in laparoscopic surgery and its certification

Simulador cardiovascular para ensayo de robots de navegación autónoma

[Resumen] Este artículo presenta un modelo de simulación del sistema cardiovascular en el entorno de Matlab/Simulink, más concretamente de la zona de mayor riesgo cardiovascular, la arteria carótida. Está basado en un modelo eléctrico del sistema que describe la dinámica de contracción del corazón, así como su carácter cíclico y autónomo. Como primer paso, este modelo se generaliza para contemplar también la dinámica de la arteria carótida izquierda. A partir de él, y haciendo una serie de equivalencias entre dominios, se obtiene un modelo hidráulico que emula el comportamiento del sistema cardiovascular en esa zona y que, a diferencia del anterior, no presenta carácter autónomo. Para el diseño del control, se hace uso de la estrategia de linealización por realimentaón. Se incluyen simulaciones, tanto del modelo eléctrico completo como del hidráulico propuesto, para demostrar el correcto funcionamiento del simulador desarrollado. El objetivo final de este trabajo es la construcción de una plataforma de ensayo para robots nadadores tipo flagelo eucariótico y bacteriano de pequeñas dimensiones a partir del modelo hidráulico desarrollado que permita emular las condiciones en las que se encontrarían estos robots navegando por el sistema circulatorio humano.Junta de Extremadura; GR15178Ministerio de Economía y Competitividad; DPI2016-80547-