SurgT challenge: Benchmark of soft-tissue trackers for robotic surgery

This paper introduces the "SurgT: Surgical Tracking" challenge which was organized in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardized benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. Participants were assigned the task of developing algorithms to track the movement of soft tissues, represented by bounding boxes, in stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge, to verify the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The metric used for ranking the methods was the Expected Average Overlap (EAO) score, which measures the average overlap between a tracker's and the ground truth bounding boxes. Coming first in the challenge was the deep learning submission by ICVS-2Ai with a superior EAO score of 0.617. This method employs ARFlow to estimate unsupervised dense optical flow from cropped images, using photometric and regularization losses. Second, Jmees with an EAO of 0.583, uses deep learning for surgical tool segmentation on top of a non-deep learning baseline method: CSRT. CSRT by itself scores a similar EAO of 0.563. The results from this challenge show that currently, non-deep learning methods are still competitive. The dataset and benchmarking tool created for this challenge have been made publicly available at https://surgt.grand-challenge.org/. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Fetal brain tissue annotation and segmentation challenge results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Smart scan of medical device displays to integrate with a mHealth application

Background

The daily monitoring of the physiological parameters is essential for monitoring health condition and to prevent health problems. This is possible due to the democratization of numerous types of medical devices and promoted by the interconnection between these and smartphones. Nevertheless, medical devices that connect to smartphones are typically limited to manufacturers applications.

Objectives

This paper proposes an intelligent scanning system to simplify the collection of data displayed on different medical devices screens, recognizing the values, and optionally integrating them, through open protocols, with centralized databases.

Methods

To develop this system, a dataset comprising 1614 images of medical devices was created, obtained from manufacturer catalogs, photographs and other public datasets. Then, three object detector algorithms (yolov3, Single-Shot Detector [SSD] 320 × 320 and SSD 640 × 640) were trained to detect digits and acronyms/units of measurements presented by medical devices. These models were tested under 3 different conditions to detect digits and acronyms/units as a single object (single label), digits and acronyms/units as independent objects (two labels), and digits and acronyms/units individually (fifteen labels). Models trained for single and two labels were completed with a convolutional neural network (CNN) to identify the detected objects. To group the recognized digits, a condition-tree based strategy on density spatial clustering was used.

Results

The most promising approach was the use of the SSD 640 × 640 for fifteen labels.

Conclusion

Lastly, as future work, it is intended to convert this system to a mobile environment to accelerate and streamline the process of inserting data into mobile health (mhealth) applications.

Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors' response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis's applied forces, corroborating its potential for clinical practice.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

Augmented Reality-Assisted Ultrasound Breast Biopsy

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician's expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.

Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors' manually handling and visually inspecting the skin. As such, the treatment outcome is dependent on the user's expertise, which frequently results in ineffective treatments and side effects. This study aims to determine the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs. The results of this study can be used to develop a robotic-guided technology to help address the aforementioned problems. Specifically, workspace and limits of operation were studied in eight vascular laser treatments. For it, an electromagnetic tracking system was used to collect the real-time positioning of the laser during the treatments. The computed average workspace length, height, and width were 0.84 ± 0.15, 0.41 ± 0.06, and 0.78 ± 0.16 m, respectively. This corresponds to an average volume of treatment of 0.277 ± 0.093 m³. The average treatment time was 23.2 ± 10.2 min, with an average laser orientation of 40.6 ± 5.6 degrees. Additionally, the average velocities of 0.124 ± 0.103 m/s and 31.5 + 25.4 deg/s were measured. This knowledge characterizes the vascular laser treatment workspace and limits of operation, which may ease the understanding for future robotic system development.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge — 2020.

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities

Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.

A review of image processing methods for fetal head and brain analysis in ultrasound images

BACKGROUND AND OBJECTIVE

Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods.

METHODS

In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement.

RESULTS

For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications.

CONCLUSIONS

A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.

Assessment of LAA Strain and Thrombus Mobility and Its Impact on Thrombus Resolution-Added-Value of a Novel Echocardiographic Thrombus Tracking Method

PURPOSE

The mobility of left atrial appendage (LAA) thrombi and changes hereof under anticoagulation may serve as a marker of both risk of embolism and efficacy of treatment. In this study, we sought to evaluate thrombus mobility and hypothesized that LAA dynamics and thrombus mobility could serve as a baseline marker of thrombus dissolvability.

METHODS

Patients with two-dimensional transesophageal echocardiographic images of the LAA, and with evidence of LAA thrombus were included in this study. Using a speckle tracking algorithm, functional information from the LAA and thrombi of different patients was computed. While the LAA motion was quantified through the longitudinal strain, thrombus mobility was evaluated using a novel method by directly tracking the thrombus, isolated from the global cardiac motion. Baseline characteristics and echocardiographic parameters were compared between responders (thrombus resolution) and non-responders (thrombus persistence) to anticoagulation.

RESULTS

We included 35 patients with atrial fibrillation with evidence of LAA thrombi. Patients had a mean age of 72.9 ± 14.1 years, exhibited a high risk for thromboembolism (CHA2DS2-VASc-Score 4.1 ± 1.5) and had moderately reduced LVEF (41.7 ± 14.4%) and signs of diastolic dysfunction (E/E' = 19.7 ± 8.5). While anticoagulation was initiated in all patients, resolution was achieved in 51.4% of patients. Significantly higher LAA peak strain (- 3.0 ± 1.3 vs. - 1.6 ± 1.5%, p < 0.01) and thrombus mobility (0.33 ± 0.13 mm vs. 0.18 ± 0.08 mm, p < 0.01) were observed in patients in whom thrombi resolved (i.e. responders against non-responders). Receiver operating characteristic (ROC) analysis revealed a high discriminatory ability for thrombus mobility with regards to thrombus resolution (AUC 0.89).

CONCLUSION

Isolated tracking of thrombus mobility from echocardiographic images is feasible. In patients with LAA thrombus, higher thrombus mobility appeared to be associated with thrombus resolution. Future studies should be conducted to evaluate the role of the described technique to predict LAA thrombus resolution or persistence.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Validation of a Novel Software Tool for Automatic Aortic Annular Sizing in Three-Dimensional Transesophageal Echocardiographic Images

BACKGROUND

Accurate aortic annulus (AoA) sizing is crucial for transcatheter aortic valve implantation planning. Three-dimensional (3D) transesophageal echocardiography (TEE) is a viable alternative to the standard multidetector row computed tomography (MDCT) for such assessment, with few automatic software solutions available. The aim of this study was to present and evaluate a novel software tool for automatic AoA sizing by 3D TEE.

METHODS

One hundred one patients who underwent both preoperative MDCT and 3D TEE were retrospectively analyzed using the software. The automatic software measurements' accuracy was compared against values obtained using standard manual MDCT, as well as against those obtained using manual 3D TEE, and intraobserver, interobserver, and test-retest reproducibility was assessed. Because the software can be used as a fully automatic or as an interactive tool, both options were addressed and contrasted. The impact of these measures on the recommended prosthesis size was then evaluated to assess if the software's automated sizes were concordant with those obtained using an MDCT- or a TEE-based manual sizing strategy.

RESULTS

The software showed very good agreement with manual values obtained using MDCT and 3D TEE, with the interactive approach having slightly narrower limits of agreement. The latter also had excellent intra- and interobserver variability. Both fully automatic and interactive analyses showed excellent test-retest reproducibility, with the first having a faster analysis time. Finally, either approach led to good sizing agreement against the true implanted sizes (>77%) and against MDCT-based sizes (>88%).

CONCLUSIONS

Given the automated, reproducible, and fast nature of its analyses, the novel software tool presented here may potentially facilitate and thus increase the use of 3D TEE for preoperative transcatheter aortic valve implantation sizing.

Technical Note: Assessment of electromagnetic tracking systems in a surgical environment using ultrasonography and ureteroscopy instruments for percutaneous renal access

PURPOSE

Electromagnetic tracking systems (EMTSs) have been proposed to assist the percutaneous renal access (PRA) during minimally invasive interventions to the renal system. However, the influence of other surgical instruments widely used during PRA (like ureteroscopy and ultrasound equipment) in the EMTS performance is not completely known. This work performs this assessment for two EMTSs [Aurora^® Planar Field Generator (PFG); Aurora^® Tabletop Field Generator (TTFG)].

METHODS

An assessment platform, composed by a scaffold with specific supports to attach the surgical instruments and a plate phantom with multiple levels to precisely translate or rotate the surgical instruments, was developed. The median accuracy and precision in terms of position and orientation were estimated for the PFG and TTFG in a surgical environment using this platform. Then, the influence of different surgical instruments (alone or together), namely analogic flexible ureterorenoscope (AUR), digital flexible ureterorenoscope (DUR), two-dimensional (2D) ultrasound (US) probe, and four-dimensional (4D) mechanical US probe, was assessed for both EMTSs by coupling the instruments to 5-DOF and 6-DOF sensors.

RESULTS

Overall, the median positional and orientation accuracies in the surgical environment were 0.85 mm and 0.42° for PFG, and 0.72 mm and 0.39° for TTFG, while precisions were 0.10 mm and 0.03° for PFG, and 0.20 mm and 0.12° for TTFG, respectively. No significant differences were found for accuracy between EMTSs. However, PFG showed a tendency for higher precision than TTFG. AUR, DUR, and 2D US probe did not influence the accuracy and precision of both EMTSs. In opposition, the 4D probe distorted the signal near the attached sensor, making readings unreliable.

CONCLUSIONS

Ureteroscopy- and ultrasonography-assisted PRA based on EMTS guidance are feasible with the tested AUR or DUR together with the 2D probe. More studies must be performed to evaluate the probes and ureterorenoscopes' influence before their use in PRA based on EMTS guidance.

Assessment of aortic valve tract dynamics using automatic tracking of 3D transesophageal echocardiographic images

The assessment of aortic valve (AV) morphology is paramount for planning transcatheter AV implantation (TAVI). Nowadays, pre-TAVI sizing is routinely performed at one cardiac phase only, usually at mid-systole. Nonetheless, the AV is a dynamic structure that undergoes changes in size and shape throughout the cardiac cycle, which may be relevant for prosthesis selection. Thus, the aim of this study was to present and evaluate a novel software tool enabling the automatic sizing of the AV dynamically in three-dimensional (3D) transesophageal echocardiography (TEE) images. Forty-two patients who underwent preoperative 3D-TEE images were retrospectively analyzed using the software. Dynamic measurements were automatically extracted at four levels, including the aortic annulus. These measures were used to assess the software's ability to accurately and reproducibly quantify the conformational changes of the aortic root and were validated against automated sizing measurements independently extracted at distinct time points. The software extracted physiological dynamic measurements in less than 2 min, that were shown to be accurate (error 2.2 ± 26.3 mm² and 0.0 ± 2.53 mm for annular area and perimeter, respectively) and highly reproducible (0.85 ± 6.18 and 0.65 ± 7.90 mm² of intra- and interobserver variability, respectively, in annular area). Using the maximum or minimum measured values rather than mid-systolic ones for device sizing resulted in a potential change of recommended size in 7% and 60% of the cases, respectively. The presented software tool allows a fast, automatic and reproducible dynamic assessment of the AV morphology from 3D-TEE images, with the extracted measures influencing the device selection depending on the cardiac moment used to perform its sizing. This novel tool may thus ease and potentially increase the observer's confidence during prosthesis' size selection at the preoperative TAVI planning.

Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions

BACKGROUND AND OBJECTIVE

Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO).

METHODS

The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strategy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location.

RESULTS

The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The automatic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO estimation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free.

CONCLUSIONS

Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice.

Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: A systematic review

BACKGROUND AND OBJECTIVE

Segmentation is an essential step in computer-aided diagnosis and treatment planning of kidney diseases. In recent years, several researchers proposed multiple techniques to segment the kidney in medical images from distinct imaging acquisition systems, namely ultrasound, magnetic resonance, and computed tomography. This article aims to present a systematic review of the different methodologies developed for kidney segmentation.

METHODS

With this work, it is intended to analyze and categorize the different kidney segmentation algorithms, establishing a comparison between them and discussing the most appropriate methods for each modality. For that, articles published between 2010 and 2016 were analyzed. The search was performed in Scopus and Web of Science using the expressions "kidney segmentation" and "renal segmentation".

RESULTS

A total of 1528 articles were retrieved from the databases, and 95 articles were selected for this review. After analysis of the selected articles, the reviewed segmentation techniques were categorized according to their theoretical approach.

CONCLUSIONS

Based on the performed analysis, it was possible to identify segmentation approaches based on distinct image processing classes that can be used to accurately segment the kidney in images of different imaging modalities. Nevertheless, further research on kidney segmentation must be conducted to overcome the current drawbacks of the state-of-the-art methods. Moreover, a standardization of the evaluation database and metrics is needed to allow a direct comparison between methods.

Fast left ventricle tracking using localized anatomical affine optical flow

In daily clinical cardiology practice, left ventricle (LV) global and regional function assessment is crucial for disease diagnosis, therapy selection, and patient follow-up. Currently, this is still a time-consuming task, spending valuable human resources. In this work, a novel fast methodology for automatic LV tracking is proposed based on localized anatomically constrained affine optical flow. This novel method can be combined to previously proposed segmentation frameworks or manually delineated surfaces at an initial frame to obtain fully delineated datasets and, thus, assess both global and regional myocardial function. Its feasibility and accuracy were investigated in 3 distinct public databases, namely in realistically simulated 3D ultrasound, clinical 3D echocardiography, and clinical cine cardiac magnetic resonance images. The method showed accurate tracking results in all databases, proving its applicability and accuracy for myocardial function assessment. Moreover, when combined to previous state-of-the-art segmentation frameworks, it outperformed previous tracking strategies in both 3D ultrasound and cardiac magnetic resonance data, automatically computing relevant cardiac indices with smaller biases and narrower limits of agreement compared to reference indices. Simultaneously, the proposed localized tracking method showed to be suitable for online processing, even for 3D motion assessment. Importantly, although here evaluated for LV tracking only, this novel methodology is applicable for tracking of other target structures with minimal adaptations.

Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35  ±  1.21 mm and 2.27  ±  1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.

A competitive strategy for atrial and aortic tract segmentation based on deformable models

Multiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors. Independent functions are used to define each region, being afterward combined to compete for the optimal boundary. The competition locally constrains the surface evolution, prevents overlaps and allows refinement to the walls. Three different scenarios were used to demonstrate the advantages of the proposed approach, through the evaluation of its segmentation accuracy, and its performance for heterogeneous mid walls. Both computed tomography and magnetic resonance imaging datasets were used, presenting results similar to the state-of-the-art methods for both atria and aorta. The competitive strategy showed its superior performance with statistically significant differences against the traditional free-evolution approach in cases with bad image quality or missed atrial/aortic walls. Moreover, only the competitive approach was able to accurately segment the atrial/aortic wall. Overall, the proposed strategy showed to be suitable for atrial region segmentation with a correct segmentation of the mid thin walls, demonstrating its added value with respect to the traditional techniques.

A new methodology for assessment of pectus excavatum correction after bar removal in Nuss procedure: Preliminary study

PURPOSE

The objective is to present a new methodology to assess quantitatively the impact of bar removal on the anterior chest wall, among patients with pectus excavatum who have undergone the Nuss procedure, and present a preliminary study using this methodology.

METHODS

We propose to acquire, for each patient, the surface of the anterior chest wall using a three-dimensional laser scanner at subsequent time points (short term: before and after surgery; long term: follow-up visit, 6months, and 12months after surgery). After surfaces postprocessing, the changes are assessed by overlapping and measuring the distances between surfaces. In this preliminary study, three time points were acquired and two assessments were performed: before vs after bar removal (early) and before vs 2-8weeks after bar removal (interim). In 21 patients, the signed distances and volumes between surfaces were computed and the data analysis was performed.

RESULTS

This methodology revealed useful for monitoring changes in the anterior chest wall. On average, the mean, maximum, and volume variations, in the early assessment, were -0.1±0.1cm, -0.6±0.2cm, and 47.8±22.2cm³, respectively; and, in the interim assessment, were -0.5±0.2cm, -1.3±0.4cm, and 122.1±47.3cm³, respectively (p<0.05). Data analysis revealed that the time the bar was in situ was inversely and significantly correlated with postretraction and was a relevant predictor of its decrease following surgery (p<0.05). Additionally, gender and age suggested influencing the outcome.

CONCLUSIONS

This methodology is novel, objective and safe, helping on follow-up of pectus excavatum patients. Moreover, the preliminary study suggests that the time the bar was in situ may be the main determinant of the anterior chest wall retraction following bar removal. Further studies should continue to corroborate and reinforce the preliminary findings, by increasing the sample size and performing long-term assessments.

LEVELS OF EVIDENCE

III.

Novel Solutions Applied in Transseptal Puncture: A Systematic Review

Access to the left atrium is required for several minimally invasive cardiac interventions in the left heart. For this purpose, transseptal puncture (TSP) technique is often performed, perforating the atrial septum under fluoroscopic or/and ultrasound imaging guidance. Although this approach has been used for many years, complications/failures are not uncommon mainly in patients with abnormal atrial anatomy and repeated TSP. Thus, this study presents an overview of methods and techniques that have been proposed to increase the safety and feasibility of the TSP. A systematic review of literature was conducted through the analysis of the articles published between 2008 and 2015. The search was performed in PubMed, Scopus, and ISI Web of Knowledge using the expression “transseptal puncture.” A total of 354 articles were retrieved from the databases, and 64 articles were selected for this review. Moreover, these 64 articles were divided into four categories, namely: (1) incidence studies, (2) intraprocedural guidance techniques, (3) preprocedural planning methods, and (4) surgical instruments. A total of 36 articles focused on incidence studies, 24 articles suggested novel intraprocedural guidance techniques, 5 works focused on preprocedural planning strategies, and 21 works proposed surgical instruments. The novel 3D guidance techniques, radio-frequency surgical instruments, and pre-interventional planning approaches showed potential to overcome the main procedural limitations/complications, through the reduction of the intervention time, radiation, number of failures, and complications.

Evaluation of spinal posture using Microsoft Kinect™: A preliminary case-study with 98 volunteers

This work proposes a novel approach to assess spinal curvature, by using Microsoft's Kinect™ to obtain 3D reconstructed models of subject's dorsal skin surface in different postures. This method is non-invasive, radiation-free and low-cost. The trial tests here presented intended to evaluate the reliability of this approach, by assessing the tendency of 98 volunteers to present scoliosis. The shoulder height difference was calculated for each subject's scan, by quantifying the angular slope of a line crossing both scapulae. The volunteers' average age was 24.7 years. Results showed that 68.37% of the volunteers revealed differences higher than 1° between the shoulders, having that their record in what concerns to loads and lesions proved to increase the angular slope. This initial approach shall establish the grounds for assessing spinal posture in pre-clinical or industrial ergonomics scans. Further studies shall include comparison versus traditional imaging methods and experienced clinical evaluation.

Dense motion field estimation from myocardial boundary displacements

Minimally invasive cardiovascular interventions guided by multiple imaging modalities are rapidly gaining clinical acceptance for the treatment of several cardiovascular diseases. These images are typically fused with richly detailed pre-operative scans through registration techniques, enhancing the intra-operative clinical data and easing the image-guided procedures. Nonetheless, rigid models have been used to align the different modalities, not taking into account the anatomical variations of the cardiac muscle throughout the cardiac cycle. In the current study, we present a novel strategy to compensate the beat-to-beat physiological adaptation of the myocardium. Hereto, we intend to prove that a complete myocardial motion field can be quickly recovered from the displacement field at the myocardial boundaries, therefore being an efficient strategy to locally deform the cardiac muscle. We address this hypothesis by comparing three different strategies to recover a dense myocardial motion field from a sparse one, namely, a diffusion-based approach, thin-plate splines, and multiquadric radial basis functions. Two experimental setups were used to validate the proposed strategy. First, an in silico validation was carried out on synthetic motion fields obtained from two realistic simulated ultrasound sequences. Then, 45 mid-ventricular 2D sequences of cine magnetic resonance imaging were processed to further evaluate the different approaches. The results showed that accurate boundary tracking combined with dense myocardial recovery via interpolation/diffusion is a potentially viable solution to speed up dense myocardial motion field estimation and, consequently, to deform/compensate the myocardial wall throughout the cardiac cycle. Copyright © 2015 John Wiley & Sons, Ltd.

Targeting lactate transport suppresses in vivo breast tumour growth

BACKGROUND

Most cancers, including breast cancer, have high rates of glucose consumption, associated with lactate production, a process referred as "Warburg effect". Acidification of the tumour microenvironment by lactate extrusion, performed by lactate transporters (MCTs), is associated with higher cell proliferation, migration, invasion, angiogenesis and increased cell survival. Previously, we have described MCT1 up-regulation in breast carcinoma samples and demonstrated the importance of in vitro MCT inhibition. In this study, we performed siRNA knockdown of MCT1 and MCT4 in basal-like breast cancer cells in both normoxia and hypoxia conditions to validate the potential of lactate transport inhibition in breast cancer treatment.

RESULTS

The effect of MCT knockdown was evaluated on lactate efflux, proliferation, cell biomass, migration and invasion and induction of tumour xenografts in nude mice. MCT knockdown led to a decrease in in vitro tumour cell aggressiveness, with decreased lactate transport, cell proliferation, migration and invasion and, importantly, to an inhibition of in vivo tumour formation and growth.

CONCLUSIONS

This work supports MCTs as promising targets in cancer therapy, demonstrates the contribution of MCTs to cancer cell aggressiveness and, more importantly, shows, for the first time, the disruption of in vivo breast tumour growth by targeting lactate transport.

Assessment of Laparoscopic Skills Performance

Introduction and Objectives. Laparoscopic surgery has undeniable advantages, such as reduced postoperative pain, smaller incisions, and faster recovery. However, to improve surgeons’ performance, ergonomic adaptations of the laparoscopic instruments and introduction of robotic technology are needed. The aim of this study was to ascertain the influence of a new hand-held robotic device for laparoscopy (HHRDL) and 3D vision on laparoscopic skills performance of 2 different groups, naïve and expert. Materials and Methods. Each participant performed 3 laparoscopic tasks—Peg transfer, Wire chaser, Knot—in 4 different ways. With random sequencing we assigned the execution order of the tasks based on the first type of visualization and laparoscopic instrument. Time to complete each laparoscopic task was recorded and analyzed with one-way analysis of variance. Results. Eleven experts and 15 naïve participants were included. Three-dimensional video helps the naïve group to get better performance in Peg transfer, Wire chaser 2 hands, and Knot; the new device improved the execution of all laparoscopic tasks ( P < .05). For expert group, the 3D video system benefited them in Peg transfer and Wire chaser 1 hand, and the robotic device in Peg transfer, Wire chaser 1 hand, and Wire chaser 2 hands ( P < .05). Conclusion. The HHRDL helps the execution of difficult laparoscopic tasks, such as Knot, in the naïve group. Three-dimensional vision makes the laparoscopic performance of the participants without laparoscopic experience easier, unlike those with experience in laparoscopic procedures.

Automatic Prebent Customized Prosthesis for Pectus Excavatum Minimally Invasive Surgery Correction

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients’ sides. It highlighted that the prosthesis bar should be modeled according to each patient’s rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Variations of the soft tissue thicknesses external to the ribs in pectus excavatum patients

BACKGROUND

Surgical repair of pectus excavatum (PE) has become more popular due to improvements in the minimally invasive Nuss procedure. The pre-surgical assessment of PE patients requires Computerized Tomography (CT), as the malformation characteristics vary from patient to patient.

OBJECTIVE

This work aims to characterize soft tissue thickness (STT) external to the ribs among PE patients. It also presents a comparative analysis between the anterior chest wall surface before and after surgical correction.

METHODS

Through surrounding tissue segmentation in CT data, STT values were calculated at different lines along the thoracic wall, with a reference point in the intersection of coronal and median planes. The comparative analysis between the two 3D anterior chest surfaces sets a surgical correction influence area (SCIA) and a volume of interest (VOI) based on image processing algorithms, 3D surface algorithms, and registration methods.

RESULTS

There are always variations between left and right side STTs (2.54 ± 2.05 mm and 2.95 ± 2.97 mm for female and male patients, respectively). STTs are dependent on age, sex, and body mass index of each patient. On female patients, breast tissue induces additional errors in bar manual conception. The distances starting at the deformity's largest depression point at the SCIA are similar in all directions. Some diverging measures and outliers were found, being difficult to find similar characteristics between them, especially in asymmetric patients.

CONCLUSION

The Nuss procedure metal bar must be modeled according to each patient's special characteristics. The studied relationships between STT and chest surface could represent a step forward to eliminate the CT scan from PE pre-surgical evaluation.

Collecting system percutaneous access using real-time tracking sensors: first pig model in vivo experience

PURPOSE

Precise needle puncture of the renal collecting system is an essential but challenging step for successful percutaneous nephrolithotomy. We evaluated the efficiency of a new real-time electromagnetic tracking system for in vivo kidney puncture.

MATERIALS AND METHODS

Six anesthetized female pigs underwent ureterorenoscopy to place a catheter with an electromagnetic tracking sensor into the desired puncture site and ascertain puncture success. A tracked needle with a similar electromagnetic tracking sensor was subsequently navigated into the sensor in the catheter. Four punctures were performed by each of 2 surgeons in each pig, including 1 each in the kidney, middle ureter, and right and left sides. Outcome measurements were the number of attempts and the time needed to evaluate the virtual trajectory and perform percutaneous puncture.

RESULTS

A total of 24 punctures were easily performed without complication. Surgeons required more time to evaluate the trajectory during ureteral than kidney puncture (median 15 seconds, range 14 to 18 vs 13, range 11 to 16, p=0.1). Median renal and ureteral puncture time was 19 (range 14 to 45) and 51 seconds (range 45 to 67), respectively (p=0.003). Two attempts were needed to achieve a successful ureteral puncture. The technique requires the presence of a renal stone for testing.

CONCLUSIONS

The proposed electromagnetic tracking solution for renal collecting system puncture proved to be highly accurate, simple and quick. This method might represent a paradigm shift in percutaneous kidney access techniques.

Neuron-specific proteotoxicity of mutant ataxin-3 in C. elegans: rescue by the DAF-16 and HSF-1 pathways

The risk of developing neurodegenerative diseases increases with age. Although many of the molecular pathways regulating proteotoxic stress and longevity are well characterized, their contribution to disease susceptibility remains unclear. In this study, we describe a new Caenorhabditis elegans model of Machado-Joseph disease pathogenesis. Pan-neuronal expression of mutant ATXN3 leads to a polyQ-length dependent, neuron subtype-specific aggregation and neuronal dysfunction. Analysis of different neurons revealed a pattern of dorsal nerve cord and sensory neuron susceptibility to mutant ataxin-3 that was distinct from the aggregation and toxicity profiles of polyQ-alone proteins. This reveals that the sequences flanking the polyQ-stretch in ATXN3 have a dominant influence on cell-intrinsic neuronal factors that modulate polyQ-mediated pathogenesis. Aging influences the ATXN3 phenotypes which can be suppressed by the downregulation of the insulin/insulin growth factor-1-like signaling pathway and activation of heat shock factor-1.