Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features

Recognizing the phases of a laparoscopic surgery (LS) operation form its video constitutes a fundamental step for efficient content representation, indexing and retrieval in surgical video databases. In the literature, most techniques focus on phase segmentation of the entire LS video using hand-crafted visual features, instrument usage signals, and recently convolutional neural networks (CNNs). In this paper we address the problem of phase recognition of short video shots (10s) of the operation, without utilizing information about the preceding/forthcoming video frames, their phase labels or the instruments used. We investigate four state-of-the-art CNN architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature extraction via transfer learning. Visual saliency was employed for selecting the most informative region of the image as input to the CNN. Video shot representation was based on two temporal pooling mechanisms. Most importantly, we investigate the role of 'elapsed time' (from the beginning of the operation), and we show that inclusion of this feature can increase performance dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory (LSTM) network was trained for video shot classification based on the fusion of CNN features with 'elapsed time', increasing the accuracy to 86%. Our results highlight the prominent role of visual saliency, long-range temporal recursion and 'elapsed time' (a feature so far ignored), for surgical phase recognition.Comment: 6 pages, 4 figures, 6 table

Machine learning with different digital images classification in laparoscopic surgery

The evaluation of the effectiveness of the automatic computer diagnostic (ACD) systems developed based on two classifiers – HAAR features cascade and AdaBoost for the laparoscopic diagnostics of appendicitis and ovarian cysts in women with chronic pelvic pain is presented. The training of HAAR features cascade, and AdaBoost classifiers were performed with images/ frames, which have been extracted from video gained in laparoscopic diagnostics. Both gamma-corrected RGB and RGB converted into HSV frames were used for training. Descriptors were extracted from images with the method of Local Binary Pattern (LBP), which includes both data on color characteristics («modified color LBP» - MCLBP) and textural characteristics, which have been used later on for AdaBoost classifier training. Classification of test video images revealed that the highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBP descriptors extracted from RGB images – 0.708, and in the case of ovarian cysts diagnostics – for MCLBP gained from RGB images – 0.886. Developed AdaBoost-based ACD system achieved a 73.6% correct classification rate (accuracy) for appendicitis and 85.4% for ovarian cysts. The accuracy of the HAAR features classifier was highest in the case of ovarian cysts identification and achieved 0.653 (RGB) – 0.708 (HSV) values. It was concluded that the HAAR feature-based cascade classifier turned to be less effective when compared with the AdaBoost classifier trained with MCLBP descriptors. Ovarian cysts were better diagnosed when compared with appendicitis with the developed ACD

Recent Advances in Minimally Invasive Surgery

Minimally invasive surgery has become a common term in visceral as well as gynecologic surgery. It has almost evolved into its own surgical speciality over the past 20 years. Today, being firmly established in every subspeciality of visceral surgery, it is now no longer a distinct skillset, but a fixed part of the armamentarium of surgical options available. In every indication, the advantages of a minimally invasive approach include reduced intraoperative blood loss, less postoperative pain, and shorter rehabilitation times, as well as a marked reduction of overall and surgical postoperative morbidity. In the advent of modern oncologic treatment algorithms, these effects not only lower the immediate impact that an operation has on the patient, but also become important key steps in reducing the side-effects of surgery. Thus, they enable surgery to become a module in modern multi-disciplinary cancer treatment, which blends into multimodular treatment options at different times and prolongs and widens the possibilities available to cancer patients. In this quickly changing environment, the requirement to learn and refine not only open surgical but also different minimally invasive techniques on high levels deeply impact modern surgical training pathways. The use of modern elearning tools and new and praxis-based surgical training possibilities have been readily integrated into modern surgical education,which persists throughout the whole surgical career of modern gynecologic and visceral surgery specialists

ПОРІВНЯЛЬНА ЕФЕКТИВНІСТЬ КЛАСИФІКАТОРІВ ЗОБРАЖЕНЬ ПІД ЧАС РОЗПІЗНАВАННЯ ЗОН ІНТЕРЕСУ ПРИ ЛАПАРОСКОПІЧНИХ ВТРУЧАННЯХ

Background. The purpose of the study is to evaluate the effectiveness of the automatic computer diagnostic (ACD) systems developed on the basis of two classifiers — HAAR features cascade and AdaBoost for the detection of appendicitis and metastatic damages of the liver. Materials and methods. For the classifiers training the images/frames, which have been cropped out from video gained in the course of laparoscopic diagnostics were used. Namely, RGB frames, and gamma-corrected RGB frames and converted into HSV have been explored. Also descriptors were extracted from images with the modified method of Local Binary Pattern (LBT), which includes data on color characteristics («modified color LBT» — MCLBT) and textural ones were used later on for AdaBoost classifier training. After cessation of training the tests were performed with the aim of the estimation of effectiveness of recognition. Test session images were different from those ones which have been used for training of the classifier. Results. The highest recall for appendicitis diagnostics was achieved after training of AdaBoost with MCLBT descriptors extracted from RGB images—0,745, and in case for metastatic damages diagnostics — 0,902. Hence developed AdaBoost based CAD system achieved 74,4 % correct classification rate (accuracy) for appendicitisc and 89,3 % for metastatic images. The accuracy of HAAR features classifier was highest in case of metastatic foci identification and achieved 0,672 (RGB) — 0,723 (HSV) values. Conclusions. Haar features based cascade classifier turned to be less effective when compared with AdaBoost classifier trained with MCLBT descriptors.В работе представлено сравнительные оценки эффективности систем автоматизированной компьютерной диагностики (АКД), разработанных на основе двух классификаторов — каскада дескрипторов Хаара и AdaBoost, во время лапароскопической диагностики аппендицита и метастазов печени. Для обучения применяли изображения, а также гамма-коррегированные и конвертированные в HSV шкалу RGB изображения, полученные при лапароскопической диагностике. Дескрипторы, которые использовали для обучения классификатора AdaBoost получали при помощи метода локального бинарного паттерна (ЛБП), который включал информационные показатели цвета («модифицированный цвет ЛБП» — MЦЛБП), а также показатели текстуры. После завершения обучения проводили тест оценки эффективности диагностики, при котором использовали изображения, неприменяемые для обучения. Наиболее высоким показатель полноты (recall) был при тестовой диагностике аппендицита с помощью классификатора AdaBoost в обучении применяли дескрипторы МЦЛБП, полученные при анализе RGB изображений — 0,745, а при диагностике метастазов печени — 0,902. Также корректность диагностики (accuracy) составила 74,4 % при диагностике аппендицита и 89,3 % при диагностике метастазов печени. Корректность диагностики при применении классификатора Хаара была наиболее высокой при диагностике метастазов печени и составила 0,672 при использовании RGB изображений и 0,723 — при обучении HSV изображениями. Диагностика с применением классификатора Хаара менее эффективна по сравнению с диагностикой, осуществляемой с применением классификатора AdaBoost, обучение последнего проводили с применением дескрипторов МЦЛБП.У роботі представлено порівняльне оцінювання ефективності систем автоматизованої комп'ютерної діагностики, розроблених на основі двох класифікаторів — каскаду дескрипторів Хаара та AdaBoost, під час лапароскопічної діагностики апендициту та метастазів печінки. Для навчання використовували зображення, а також гама-кореговані та конвертовані у HSV шкалу кольори RGB зображення, отримані під час лапароскопічної діагностики. Дескриптори, що використовували для навчання класифікатора AdaBoost отримували за допомогою методу локального бінарного патерну, який включав інформаційні показники кольору, а також показники текстури. Після завершення навчання проводили тест оцінювання ефективності діагностики при якому використовували зображення, що не застосовували для навчання. Найбільш високим показник повноти (recall) був при тестовій діагностиці апендициту за допомогою навчання класифікатора AdaBoost дескрипторами модифікованого кольору локального бінарного патерну, отриманими з RGB зображень, — 0,745, а під час діагностики метастазів печінки — 0,902. Також коректність діагностики (accuracy) склала 74,4 % під час діагностики апендициту та 89,3 % при діагностиці метастазів печінки. Коректність діагностики із застосуванням класифікатора Хаара була найбільш високою за умови діагностики метастазів печінки та склала 0,672 при використанні RGB зображень, 0,723 — при навчанні HSV зображеннями. Діагностика із застосуванням класифікатора Хаара є менш ефективною порівняно з діагностикою, що здійснювалась із застосуванням класифікатора AdaBoost, навчання якого здійснювали із застосуванням дескрипторів модифікованого кольору локального бінарного патерну

Artificial intelligence surgery: how do we get to autonomous actions in surgery?

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner

Safety and Efficacy of Laparoscopic Colposuspension

Objective:The aim of the study is to determine the effectiveness of the laparoscopic colposuspension in the treatment of urinary stress incontinence. The secondary purpose is to define the place of the intervention among the contemporary methods for the treatment of the disease. Material and methods: We conducted a prospective single arm observational study between April 1993 and April 2000. All patients participated following a written informed consent. The patients included in this biggest study ever, suffered of urinary stress incontinence or mixed incontinence and we used the laparoscopic colposuspension for the treatment of the disorder. The cure rate was evaluated objectively based on personal examination, and subjectively using an “Incontinence Questionnaire”, filled out by the patients postoperatively. Results: Out of 312 patients, 7.2% had preoperatively a USI I (urinary stress incontinence grade I), 23.1% a USI II and 69.7% a USI III. Mixed incontinence was observed in 41.1% of the patients and preoperative recurrent incontinence in 17.6% of them. The laparoscopic colposuspension alone was performed in 131 cases and combined with other surgical interventions in 181 patients. The cure rates achieved in our study were 86.4% in primary incontinence and 62.5% in the recurrent urinary incontinence. The overall complication rate was 11.2% with 6% major complications. Conclusions: The high cure rates obtained in this study, sustained by the literature results, support the further recommendation of the laparoscopic colposuspension in the treatment of urinary stress incontinence as a primary as well as an alternative operative technique

A comprehensive survey on recent deep learning-based methods applied to surgical data

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery.Comment: This paper is to be submitted to International journal of computer visio

Faulty femininity

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