Manual Marking Research Articles

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields—including maritime traffic survey and maritime rescue—it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea–land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea–land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

Conditional Clauses in Sign Language of the Netherlands: A Corpus-Based Study

Conditional clauses are underresearched in sign languages, and the research that has been done has mostly employed elicited data. The current study provides a thorough description of conditional clauses in Sign Language of the Netherlands (NGT) based on naturalistic corpus data. Similar to conditionals in other sign languages, conditionals in NGT can be introduced with a manual marker, although it is also possible to only use nonmanual markers such as raised eyebrows, head movement, and head tilt. Different, however, is the striking amount of variation that we found with respect to both manual and nonmanual marking. This might be due to differing methodologies, but we discuss several other potential explanations as well. We further provide evidence that nonmanual markers are less frequent in conditionals with manual markers than without. In addition, we offer an—albeit brief—description of peripheral conditionals in NGT, a conditional type that has not yet been investigated thoroughly for any sign language.

Anatomical and functional outcomes of toric intraocular lens implantation

Background: To minimize the residual refractive correction, toric intraocular lens (TIOL) is being increasingly used in cases undergoing cataract surgery with preexisting corneal astigmatism of ≥1.0 D. Aims: The aim of this study is to evaluate anatomical and functional outcomes of TIOL implantation in cases undergoing cataract surgery with significant corneal astigmatism using conventional manual marking of target axis and to see for any difference in outcomes based on the power of TIOL. Methodology: It is a prospective observational study. Consecutive patients with cataract and astigmatism of >1.0 D with adequate pupil dilation and no significant ocular or systemic comorbidity scheduled to undergo phacoemulsification with TIOL implantation were enrolled. Results: The mean improvement in postoperative uncorrected distance visual acuity (UDVA) over preoperative corrected distance visual acuity was 0.51 ± 0.54 logarithm of the minimum angle of resolution (95% confidence interval CI: 0.30–0.70), equivalent to 4 Snellen lines. The mean postoperative spherical equivalent is 0.38 ± 0.27; 77.4% showed within 0.50 D and the rest were within 1 D. The mean deviation of postoperative TIOL alignment from intended intraoperative TIOL alignment is 3° ± 2° (range: 0°–6°). Both the groups (low- and medium-power TIOLs) had similar postoperative UDVA, residual astigmatism, and deviation of target axis. Conclusions: There was an effective correction of preoperative corneal astigmatism and good visual outcomes with TIOL implantation. Fairly accurate TIOL alignment is achievable with manual marking of target axis. The power of TIOL did not influence postoperative UDVA, residual astigmatism, or deviation of TIOL from target axis.

New electrical device for field coincidence quality control test in linear accelerators

Testing of the coincidence of a linear accelerators X-ray field and the light field as a quality control test is often done in a subjective method, involving the manual marking of pieces of film and the visual inspection of the film after irradiation. The purpose of this study was to develop an objective method for performing this test, while also increasing the accuracy, precision and the speed of the test. The new method uses a portable, lightweight an inexpensive device containing optically-shielded and nonoptically-shielded photo diodes to detect the location and dimensions of the light and x-ray fields. The measurement results are analyzed and drawn using purpose-written user friendly software. This system is able to reliably measure the coincidence of the two fields with 0.5 mm accuracy and a standard deviation of 0.02 mm, which is well within the tolerance recommended by AAPM Task Group 42. This proposed method allows accurate and precise measurements to be obtained, through a largely automated process, resulting in measurements that have substantially reduced sensitivity to subjectivity or human error, compared with the standard film-based technique.

Attribute-Based Fused Feature for Person Re-identification

In this paper, we propose an attribute-based fused feature which combined low-level and attribute features for person re-identification. Pedestrian parsing and different low-light image enhancement methods are adopted before feature extraction and Random Forest (RF) classifier is used as the basic classifier. Firstly, each of the image is divided into eight strips, and the strips are composed into four parts: head, up-body, low-body and bag areas. Above these four parts, the gradient-LOMO (Local Maximal Occurrence) features are extracted separately to form sub-classifiers features. The sub-classifiers of the independent and different dimensional attributes are respectively trained and then fused to generate a classifier, which describes 21-dimensional attribute features and is combined with the correction mechanism. Then, this classifier can be used to predict attributes on unmarked datasets. In this way, the time complexity of manual marking can be reduced effectively. Finally, the low-level features, which extracted from original and foreground images, are combined with attributes as the fused feature. The results of experiments on three common person re-identification datasets (VIPeR, PRID450S and GRID), indicate that our attribute-based fused feature exhibits prominently performance which outperforms the state-of-the-art methods for person re-identification.

Dataset and Metrics for Predicting Local Visible Differences

A large number of imaging and computer graphics applications require localized information on the visibility of image distortions. Existing image quality metrics are not suitable for this task as they provide a single quality value per image. Existing visibility metrics produce visual difference maps, and are specifically designed for detecting just noticeable distortions but their predictions are often inaccurate. In this work, we argue that the key reason for this problem is the lack of large image collections with a good coverage of possible distortions that occur in different applications. To address the problem, we collect an extensive dataset of reference and distorted image pairs together with user markings indicating whether distortions are visible or not. We propose a statistical model that is designed for the meaningful interpretation of such data, which is affected by visual search and imprecision of manual marking. We use our dataset for training existing metrics and we demonstrate that their performance significantly improves. We show that our dataset with the proposed statistical model can be used to train a new CNN-based metric, which outperforms the existing solutions. We demonstrate the utility of such a metric in visually lossless JPEG compression, super-resolution and watermarking.

Initial Experience Using AutoMark in Atrial Fibrillation Ablation

Background: EnSite Precision AutoMark Module (Abbott) refers to automated lesion creating tool. Only lesions meeting user-defined requirements are placed on the map. The amount of ablation is represented by the size and color of marking spheres. The aim of this study is to present our initial experience with AutoMark Module in AF ablation. Methods: Nine patients with AF underwent electroanatomic mapping and radiofrequency ablation (RF) using AutoMark Module for marking RF lesions. Pulmonary vein isolation was performed in all patients. Cavo tricuspid isthmus ablation was done in 3 patients with atrial flutter. Lesions were marked in different colors depending on force-time integral, absolute impedance drop and RF duration as follows: white (10 Ω; >30 s). An auto-mark was created when catheter remained in a stable location for more than 3 s, with a minimum distance between spheres (center-to-center) of 3 mm. Auto-marks were compared with manually placed spheres. 24-hour Holter ECG monitoring was performed regularly until the end of the follow-up. Results: Nine patients (mean age of 53 ± 10 years, 3 male) were included in the study with EHRA class 2b (44%) and class 3 (56%). Auto-marks were placed only when user-defined criteria were reached whereas manual points were marked subjectively. The auto-marks numbered 299 ± 82 per procedure and 5.39 ± 1.42 per RF application. Auto-marks provided good visualization of the ablation lines using the predefined criteria. AutoMark Module visualized probable gaps in the ablation line, which appeared as an area without spheres or with white spheres. That was not possible with manual marking because of its subjective nature. Conclusion: AutoMark is better than manual marking in providing real-time visual feedback on lesion creation. Our preselected lesion parameters ensure a tool to visualize possible gaps in the ablation line.

Subtle changes in diabetic retinas localised in 3D using OCT.

To detect and localise subtle changes in retinas of diabetic patients who clinically have no diabetic retinopathy (DR) or non-proliferative DR (NPDR) as compared to age- and sex- matched controls. Spectral Domain Optical Coherence Tomography (SD-OCT) and software to examine all retinal layers, including deeper layers, were used to quantify foveal avascular zone size and inner and outer retinal layer thicknesses, as well as to detect axial location of prominent lesions. Diabetic subjects, 19 total with 16 having no DR and three having non-proliferative retinopathy, were matched with 19 controls with respect to age and sex. Macular-centred SD-OCT grids of 20 × 15° were taken with the Spectralis. En face or transverse images were generated from the SD-OCT data by automatically segmenting all retinal layers. The transverse images were investigated for foveal avascular zone (FAZ) size, retinal vessel calibre, and structural changes. The size of the FAZ was compared for diabetics vs controls using vendor software and manual marking in Photoshop. Inner retinal layer (IRLFAZ ) and outer nuclear layer (ONLFAZ ) thicknesses at the margins of the FAZ were measured using vendor software. The FAZ area was larger for diabetics (mean ± S.D. = 0.388 ± 0.074 mm2 ) than controls (0.243 ± 0.113 mm2 ), t18 = 5.27, p < 0.0001, using vendor software. The mean IRLFAZ was thicker for the diabetics (86.8 ± 14.5 μm) than controls (65.2 ± 16.3 μm), t18 = 4.59, p = 0.00023, despite lack of exudation by clinical exam. There was no significant association between FAZ area and mean IRLFAZ for the diabetics, r = 0.099, p = 0.69. Vessels not clinically detected were visible in the NFL transverse image of most diabetics, especially for a mild NPDR patient. A prominent lesion found in the en face infra-red image of a mild NPDR subject was localised in the photoreceptor layer by SD-OCT, as well as additional outer retinal changes in other subjects. Our results demonstrate changes in inner and outer diabetic retinas not readily detectable by clinical exam. IRLFAZ had not thinned at the margins of the large FAZs, indicating neural mass did not yet decrease despite potential ischemia.

Extraction of Aortic Knuckle Contour in Chest Radiographs Using Deep Learning.

In this paper, we aim to extract the aortic knuckle (AK) contour in chest radiographs, an anatomical structure rarely being addressed in the literature. Since the AK structure is small and thin, simply adopting the deep network methods that are successful for large organ segmentation is inadequate for achieving good pixel-level accuracy and resolving local ambiguities. To address this challenge, we propose a new coarse-to-fine segmentation approach which focuses on global and local information contexts, respectively. Two convolutional networks are used. For the coarse segmentation, we use FasterRCNN; for the fine segmentation, we use U-Net. Our evaluation uses the publicly available JSRT dataset; the results are promising. Besides presenting these results, we analyze issues such as the imprecision of manual contour marking, and automatic generation of the coarse segmentation ground-truth mask used for deep network training. Our approach is general and can be applied to extract other curve-like objects-of-interest.

Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule.

Interictal spikes (IIS) are bursts of neuronal depolarization observed electrographically between periods of seizure activity in epilepsy patients. However, IISs are difficult to characterize morphologically and their effects on neurophysiology and cognitive function are poorly understood. Currently, IIS detection requires laborious manual assessment and marking of electroencephalography (EEG/iEEG) data. This practice is also subjective as the clinician has to select the mental threshold that EEG activity must exceed in order to be considered a spike. The work presented here details the development and implementation of a simple automated IIS detection algorithm. This preliminary study utilized intracranial EEG recordings collected from 7 epilepsy patients, and IISs were marked by a single physician for a total of 1339 IISs across 68 active electrodes. The proposed algorithm implements a simple threshold rule that scans through iEEG data and identifies IISs using various normalization techniques that eliminate the need for a more complex detector. The efficacy of the algorithm was determined by evaluating the sensitivity and specificity of the detector across a range of thresholds, and an approximate optimal threshold was determined using these results. With an average true positive rate of over 98% and a false positive rate of below 2%, the accuracy of this algorithm speaks to its use as a reliable diagnostic tool to detect IISs, which has direct applications in localizing where seizures start, detecting when seizures start, and in understanding cognitive impairment due to IISs. Furthermore, due to its speed and simplicity, this algorithm can be used for real-time detection of IIS that will ultimately allow physicians to study their clinical implications with high temporal resolution and individual adaptation.

Spatial agreement of demineralized areas in quantitative light-induced fluorescence images and digital photographs.

Previous work has shown qualitatively that detection of demineralized tooth areas (white spot lesions, WSLs) is more reliable in digital photographs (DP) as in quantitative light-induced fluorescence (QLF) images. Based on non-rigid, multimodal image registration, we now quantitatively compare manual and automatic markings in both modalities. After braces removal, pairs of DP and QLF were acquired from 124 teeth of 31 patients. Three experienced raters marked the WSL on both DP and QLF images, each of which was presented twice in randomized order. For each tooth and each modality, a ground truth (GT) was established using the simultaneous truth and performance level estimation algorithm on the total of six manual markings per image. DP and QLF image pairs were spatially registered, by aligning the outline of the tooth area in DPs to that of the corresponding tooth area in QLF. Between all pairs of markings for all teeth, position and size were compared quantitatively by the Dice coefficient and the novel coefficient of inclusion. Our hypotheses: (i) the clinical inspection supported by DP is more sensitive to WSL as that by QLF, disregarding whether the automatic analysis or the experts' manual assessment of QLF is applied, and (ii) detected lesions in QLF are included in those of DP, were confirmed and not confirmed, respectively. DP and QLF are valuable methods to detect WSL in demineralized teeth. Combining both modalities can provide additional information on early lesion assessment.

Multi-model induced network for participatory-sensing-based classification tasks in intelligent and connected transportation systems

Classification is an emerging Internet of Things (IoT) application in intelligent and connected transportation systems (ICTS). To reduce the costs associated with classification tasks, the concept of participatory sensing has been integrated into ICTS. In this approach, for performing classification tasks, a crowd of participants reports multi-modal data collected via smart devices and manual marking. However, the multi-modal data thus collected are always incomplete. Therefore, integrating these multi-modal data for classification in ICTS is a challenge. In this paper, we propose a multi-model induced network (MMiN) framework for participatory-sensing-based classification tasks in ICTS. We first explore the relationships of the multi-modal data through graph modeling and hypergraph learning. Then, the derived relationships and the multi-modal data are used to train the MMiN. Finally, end-to-end features are derived by the MMiN as the classification results. To evaluate the effectiveness of the MMiN framework, experiments conducted on three visual datasets and one trajectory dataset are reported. The experimental results and comparisons demonstrate the effectiveness of the MMiN framework in exploiting incomplete multi-modal data.

The segmentation of bones in pelvic CT images based on extraction of key frames

BackgroundBone segmentation is important in computed tomography (CT) imaging of the pelvis, which assists physicians in the early diagnosis of pelvic injury, in planning operations, and in evaluating the effects of surgical treatment. This study developed a new algorithm for the accurate, fast, and efficient segmentation of the pelvis.MethodsThe proposed method consists of two main parts: the extraction of key frames and the segmentation of pelvic CT images. Key frames were extracted based on pixel difference, mutual information and normalized correlation coefficient. In the pelvis segmentation phase, skeleton extraction from CT images and a marker-based watershed algorithm were combined to segment the pelvis. To meet the requirements of clinical application, physician’s judgment is needed. Therefore the proposed methodology is semi-automated.ResultsIn this paper, 5 sets of CT data were used to test the overlapping area, and 15 CT images were used to determine the average deviation distance. The average overlapping area of the 5 sets was greater than 94%, and the minimum average deviation distance was approximately 0.58 pixels. In addition, the key frame extraction efficiency and the running time of the proposed method were evaluated on 20 sets of CT data. For each set, approximately 13% of the images were selected as key frames, and the average processing time was approximately 2 min (the time for manual marking was not included).ConclusionsThe proposed method is able to achieve accurate, fast, and efficient segmentation of pelvic CT image sequences. Segmentation results not only provide an important reference for early diagnosis and decisions regarding surgical procedures, they also offer more accurate data for medical image registration, recognition and 3D reconstruction.

Deep learning based image recognition for crack and leakage defects of metro shield tunnel

The performance of traditional visual inspection by handcrafted features for crack and leakage defects of metro shield tunnel is hardly satisfactory nowadays because it is low-efficient to distinguish defects from some interference such as segmental joints, bolt holes, cables and manual marks. Based on deep learning (DL), this paper proposes a novel image recognition algorithm for semantic segmentation of crack and leakage defects of metro shield tunnel using hierarchies of features extracted by fully convolutional network (FCN). The defect images in training dataset and testing dataset are captured via a self-developed image acquisition equipment named Moving Tunnel Inspection (MTI-200a). After the establishment of image datasets, FCN models of crack and leakage are separately trained through several iterations of forward inference and backward learning. Semantic segmentation of defect images is implemented via the corresponding FCN models using two-stream algorithm, i.e. one stream is used to recognize the crack by sliding-window-assembling operation and the other is adopted for the leakage by resizing-interpolation operation. Compared with two frequently-used traditional methods, i.e. region growing algorithm (RGA) and adaptive thresholding algorithm (ATA), great superiority of the proposed method in terms of recognition results, inference time and error rates is shown based on four typical types of defect images which are crack-only image, leakage-only image, two-defect-nonoverlapping (TDN) image, two-defect-overlapping (TDO) image. The proposed method using DL can be employed to rapidly and accurately recognize defects for structure health monitoring and maintenance of metro shield tunnels.

Comparative evaluation of toric intraocular lens alignment and visual quality with image-guided surgery and conventional three-step manual marking.

PurposeTo compare toric intraocular lens (IOL) alignment assisted by image-guided surgery or manual marking methods and its impact on visual quality.Patients and methodsThis prospective comparative study enrolled 80 eyes with cataract and astigmatism ≥1.5 D to undergo phacoemulsification with toric IOL alignment by manual marking method using bubble marker (group I, n=40) or Callisto eye and Z align (group II, n=40). Postoperatively, accuracy of alignment and visual quality was assessed with a ray tracing aberrometer. Primary outcome measure was deviation from the target axis of implantation. Secondary outcome measures were visual quality and acuity. Follow-up was performed on postoperative days (PODs) 1 and 30.ResultsDeviation from the target axis of implantation was significantly less in group II on PODs 1 and 30 (group I: 5.5°±3.3°, group II: 3.6°±2.6°; p=0.005). Postoperative refractive cylinder was −0.89±0.35 D in group I and −0.64±0.36 D in group II (p=0.003). Visual acuity was comparable between both the groups. Visual quality measured in terms of Strehl ratio (p<0.05) and modulation transfer function (MTF) (p<0.05) was significantly better in the image-guided surgery group. Significant negative correlation was observed between deviation from target axis and visual quality parameters (Strehl ratio and MTF) (p<0.05).ConclusionImage-guided surgery allows precise alignment of toric IOL without need for reference marking. It is associated with superior visual quality which correlates with the precision of IOL alignment.

Implantation torique : comparaison du marquage horizontal manuel versus automatique, à propos de 50 cas

The main objective of our study is to evaluate the contribution of automated conjunctival recognition in the alignment of toric implants by comparing the automatic alignment optimized with Callisto™ to the manual marking of the horizontal axis. We performed a prospective, descriptive, and monocentric study on patients undergoing cataract surgery with toric implantation (Asphina 709Zeiss), operated by a surgeon with good experience in toricity, between September 2016and March 2017. We analyzed the agreement between the manual marking of the 0°-180° axis versus the one automatically generated by the Callisto™, as well as the alignment of the IOL and the refractive results at 1month. We included 50eyes of 26patients. The corrected mean astigmatism was 1.9D. The mean difference between the 2axes was 4.7° [0°-12.3°]. Only 50% of the preoperative manual markings were consistent with the automatic measurement (<5°). At one month, the average rotation recorded was 4.3° [0°-29°]. The alignment was identical for 70% (n=35) of the IOL (≤5°). As for residual subjective astigmatism, it was on average 0.58D. The mean visual acuity without correction was 8/10and 55% had 10/10without correction. The refractive performance depends on the preoperative measurement, the correct alignment of the IOL and its stability in the bag. Our study shows the value of automatic conjunctival recognition in the determination of the axis of peroperative alignment, even in an experienced operator. This precision is essential for a good refractive result, especially since the residual astigmatism in case of misalignment will increase with the power of the implant. Our study shows excellent refractive results, whatever the initial astigmatism, using the automatic alignment. The precision of the toric implantation opens the way to the toric multifocal implantation under the best conditions.

Improved Visualization of Gastrointestinal Slow Wave Propagation Using a Novel Wavefront-Orientation Interpolation Technique.

High-resolution mapping of gastrointestinal (GI) slow waves is a valuable technique for research and clinical applications. Interpretation of high-resolution GI mapping data relies on animations of slow wave propagation, but current methods remain as rudimentary, pixelated electrode activation animations. This study aimed to develop improved methods of visualizing high-resolution slow wave recordings that increases ease of interpretation. The novel method of "wavefront-orientation" interpolation was created to account for the planar movement of the slow wave wavefront, negate any need for distance calculations, remain robust in atypical wavefronts (i.e., dysrhythmias), and produce an appropriate interpolation boundary. The wavefront-orientation method determines the orthogonal wavefront direction and calculates interpolated values as the mean slow wave activation-time (AT) of the pair of linearly adjacent electrodes along that direction. Stairstep upsampling increased smoothness and clarity. Animation accuracy of 17 human high-resolution slow wave recordings (64-256 electrodes) was verified by visual comparison to the prior method showing a clear improvement in wave smoothness that enabled more accurate interpretation of propagation, as confirmed by an assessment of clinical applicability performed by eight GI clinicians. Quantitatively, the new method produced accurate interpolation values compared to experimental data (mean difference 0.02 ± 0.05 s) and was accurate when applied solely to dysrhythmic data (0.02 ± 0.06 s), both within the error in manual AT marking (mean 0.2 s). Mean interpolation processing time was 6.0 s per wave. These novel methods provide a validated visualization platform that will improve analysis of high-resolution GI mapping in research and clinical translation.

Tailoring four-dimensional cone-beam CT acquisition settings for fiducial marker-based image guidance in radiation therapy.

Use of four-dimensional cone-beam CT (4D-CBCT) and fiducial markers for image guidance during radiation therapy (RT) of mobile tumors is challenging due to the trade-off among image quality, imaging dose, and scanning time. This study aimed to investigate different 4D-CBCT acquisition settings for good visibility of fiducial markers in 4D-CBCT. Using these 4D-CBCTs, the feasibility of marker-based 4D registration for RT setup verification and manual respiration-induced motion quantification was investigated. For this, we applied a dynamic phantom with three different breathing motion amplitudes and included two patients with implanted markers. Irrespective of the motion amplitude, for a medium field of view (FOV), marker visibility was improved by reducing the imaging dose per projection and increasing the number of projection images; however, the scanning time was 4 to 8min. For a small FOV, the total imaging dose and the scanning time were reduced (62.5% of the dose using a medium FOV, 2.5min) without losing marker visibility. However, the body contour could be missing for a small FOV, which is not preferred in RT. The marker-based 4D setup verification was feasible for both the phantom and patient data. Moreover, manual marker motion quantification can achieve a high accuracy with a mean error of [Formula: see text].

Toric lens implantation in cataract surgery: Automated versus manual horizontal axis marking, analysis of 50 cases

The main objective of our study was to evaluate the contribution of automated conjunctival registration in the alignment of toric intraocular lenses by comparing automated registration optimized with Callisto® to manual marking of the horizontal axis. We performed a prospective, descriptive, monocentric study on patients undergoing cataract surgery with a toric intraocular lens (Asphina 709Zeiss), performed by a surgeon with good experience in toric implants, between September 2016and March 2017. We analyzed the agreement between the manual marking of the 0-180° axis versus the one automatically generated by the Callisto™, as well as the alignment of the IOL and the refractive results at 1month. We included 50eyes of 38patients. The mean corrected astigmatism was 1,9D.The mean difference between the 2axes was 4,7° [0-12.3°]. Only 50% of the preoperative manual markings were consistent with the automated measurement (<5°). At one month, the mean rotation recorded was 4,3° [0-29°]. The alignment was identical for 70% (n=35) of the IOLs (≤5°). As for residual subjective astigmatism, the mean was 0.58D.The mean visual acuity without correction was 8/10and 55% saw 10/10without correction. Refractive performance depends on preoperative measurement, correct alignment of the IOL and its stability in the bag. Our study shows the value of automated conjunctival registration in the determination of the intraoperative axis of alignment, even with an experienced surgeon. This precision is essential for a good refractive result, especially since residual astigmatism in the case of misalignment will increase with the power of the implant. Our study shows excellent refractive results, regardless of the initial astigmatism, using automated alignment. Precision of toric implantation opens the way to toric multifocal implantation under the best conditions.

Innovative Use of Medical Infrared Thermography to Evaluate Adhesive Properties of Two Buprenorphine Transdermal Patch Formulations in a Single Dose Adhesion Performance Study

An innovative approach for the assessment of transdermal patches adherence to the skin was developed and applied post-hoc to supportive imaging data collected in an open label, two-period, two-sequence, two-way crossover, controlled, randomized, single dose study to assess adhesion performance of two buprenorphine formulations (generic versus originator), applied topically to healthy male and female volunteers. The technology used for data acquisition is called Medical Infrared Thermography (MIT), a non-invasive, non-radiating imaging technique for surface temperature mapping. The use of this technology in an adhesion performance study was based on the rationale that the surface of a transdermal patch reaches a temperature in equilibrium with that of the body area where it is applied and whenever a discontinuity between skin and patch exists, the transmission of thermic energy to the surface of the transdermal patch changes. In case air is interposed, the transmission is minimal so any cases of patch lift-off from skin become visible on thermograms. The thermograms were acquired in standardized conditions but were initially intended only as supportive imaging technique, while the primary adhesion performance data was acquired through the standard approach of visual examination coupled with manual markings. In this article we further demonstrate the value of the acquired thermograms as main adhesion assessment tools, used in conjuncture with an in-house validated image processing software for the actual quantification of percentages of detachment. This novel approach for automated and unbiased analysis of adhesion performance was shown to be highly reliable and reproducible, attributes that recommend it for future use as primary adhesion assessment tool pending completion of full method validation.