Computer-assisted liver surgery: clinical applications and technological trends

Oncological liver surgery and interventions aim for removal of tumor tissue while preserving a sufficient amount of functional tissue to ensure organ regeneration. This requires detailed understanding of the patient-specific internal organ anatomy (blood vessel system, bile ducts, tumor location). The introduction of computer support in the surgical process enhances anatomical orientation through patient-specific 3D visualization and enables precise reproduction of planned surgical strategies though stereotactic navigation technology. This article provides clinical background information on indications and techniques for the treatment of liver tumors, reviews the technological contributions addressing the problem of organ motion during navigated surgery on a deforming organ, and finally presents an overview of the clinical experience in computer-assisted liver surgery and interventions. The review concludes that several clinically applicable solutions for computer aided liver surgery are available and small-scale clinical trials have been performed. Further developments will be required more accurate and faster handling of organ deformation and large clinical studies will be required for demonstrating the benefits of computer aided liver surgery.

Russian language maintenance through bedtime story reading?: Linguistic strategies and language negotiation in Russian-French speaking families in Switzerland

This article proposes an interactional approach to the question of Russian language maintenance through the activity of bedtime story-reading in Russian-French bilingual families in French speaking Switzerland. Reading stories appears to be a language maintenance strategy commonly employed by the Russian speaking parent. The ritual and recreational moment of story-reading therefore becomes an opportunity for language learning. Drawing upon a language socialization perspective, this paper proposes an interactional analysis of the language use in the activity of story-reading. It shows how the language choice of the participants may be requested, negotiated and challenged during the interaction. The analysis further informs us about the language choice pattern and the bilingual competences in these families. We will gain insight into (Russian) language maintenance as a daily social and linguistic practice.

Lung cancer screening with CT: evaluation of radiologists and different computer assisted detection software (CAD) as first and second readers for lung nodule detection at different dose levels

OBJECTIVES To find the best pairing of first and second reader at highest sensitivity for detecting lung nodules with CT at various dose levels. MATERIALS AND METHODS An anthropomorphic lung phantom and artificial lung nodules were used to simulate screening CT-examination at standard dose (100 mAs, 120 kVp) and 8 different low dose levels, using 120, 100 and 80 kVp combined with 100, 50 and 25 mAs. At each dose level 40 phantoms were randomly filled with 75 solid and 25 ground glass nodules (5-12 mm). Two radiologists and 3 different computer aided detection softwares (CAD) were paired to find the highest sensitivity. RESULTS Sensitivities at standard dose were 92%, 90%, 84%, 79% and 73% for reader 1, 2, CAD1, CAD2, CAD3, respectively. Combined sensitivity for human readers 1 and 2 improved to 97%, (p1=0.063, p2=0.016). Highest sensitivities--between 97% and 99.0%--were achieved by combining any radiologist with any CAD at any dose level. Combining any two CADs, sensitivities between 85% and 88% were significantly lower than for radiologists combined with CAD (p<0.03). CONCLUSIONS Combination of a human observer with any of the tested CAD systems provide optimal sensitivity for lung nodule detection even at reduced dose at 25 mAs/80 kVp.

Optimal dose levels in screening chest CT for unimpaired detection and volumetry of lung nodules, with and without computer assisted detection at minimal patient radiation

OBJECTIVES The aim of this phantom study was to minimize the radiation dose by finding the best combination of low tube current and low voltage that would result in accurate volume measurements when compared to standard CT imaging without significantly decreasing the sensitivity of detecting lung nodules both with and without the assistance of CAD. METHODS An anthropomorphic chest phantom containing artificial solid and ground glass nodules (GGNs, 5-12 mm) was examined with a 64-row multi-detector CT scanner with three tube currents of 100, 50 and 25 mAs in combination with three tube voltages of 120, 100 and 80 kVp. This resulted in eight different protocols that were then compared to standard CT sensitivity (100 mAs/120 kVp). For each protocol, at least 127 different nodules were scanned in 21-25 phantoms. The nodules were analyzed in two separate sessions by three independent, blinded radiologists and computer-aided detection (CAD) software. RESULTS The mean sensitivity of the radiologists for identifying solid lung nodules on a standard CT was 89.7% ± 4.9%. The sensitivity was not significantly impaired when the tube and current voltage were lowered at the same time, except at the lowest exposure level of 25 mAs/80 kVp [80.6% ± 4.3% (p = 0.031)]. Compared to the standard CT, the sensitivity for detecting GGNs was significantly lower at all dose levels when the voltage was 80 kVp; this result was independent of the tube current. The CAD significantly increased the radiologists' sensitivity for detecting solid nodules at all dose levels (5-11%). No significant volume measurement errors (VMEs) were documented for the radiologists or the CAD software at any dose level. CONCLUSIONS Our results suggest a CT protocol with 25 mAs and 100 kVp is optimal for detecting solid and ground glass nodules in lung cancer screening. The use of CAD software is highly recommended at all dose levels.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.