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Purpose: We present a markerless vision-based method for on-the-fly three-dimensional (3D) pose estimation of a fiberscope instrument to target pathologic areas in the endoscopic view during exploration. Approach: A 2.5-mm-diameter fiberscope is inserted through the endoscope’s operating channel and connected to an additional camera to perform complementary observation of a targeted area such as a multimodal magnifier. The 3D pose of the fiberscope is estimated frame-by-frame by maximizing the similarity between its silhouette (automatically detected in the endoscopic view using a deep learning neural network) and a cylindrical shape bound to a kinematic model reduced to three degrees-of-freedom. An alignment of the cylinder axis, based on Plücker coordinates from the straight edges detected in the image, makes convergence faster and more reliable. Results: The performance on simulations has been validated with a virtual trajectory mimicking endoscopic exploration and on real images of a chessboard pattern acquired with different endoscopic configurations. The experiments demonstrated a good accuracy and robustness of the proposed algorithm with errors of [Formula: see text] in distance position and [Formula: see text] in axis orientation for the 3D pose estimation, which reveals its superiority over previous approaches. This allows multimodal image registration with sufficient accuracy of [Formula: see text]. Conclusion: Our pose estimation pipeline was executed on simulations and patterns; the results demonstrate the robustness of our method and the potential of fiber-optical instrument image-based tracking for pose estimation and multimodal registration. It can be fully implemented in software and therefore easily integrated into a routine clinical environment.

Cutaneous Leishmaniasis is a skin infection which is commonly present in underdeveloped countries. The incidence is particularly high in amazonic countries of Latin-America like Brazil, Colombia and Perú and is usually reported as endemic. In Perú, more than one million people are at risk of infection and approximately 6,000 new cases are detected each year. The present work proposes to integrate a set of control, monitoring and disease quantification procedures in: (1) an automated tool to expedite the analysis in laboratories studying parasiticidal agents and (2) a non-invasive treatment monitoring protocol. The first, consists in the adaptation of an optical microscope KRUSS MBL3100 to perform a fast image capture and automated promastigotes identification. This may be of value to evaluate the effectiveness of various parasiticidal agents. The counting process is performed by an automatic segmentation in the RGB green color space discriminating elements by their area. The second, proposes a protocol for monitoring the evolution of the disease treatment divided into three stages: supra-skin modeling and reconstruction, subcutaneous exploration by textural characteristics and volumetric segmentation. This protocol is performed using a Next Engine HD 3D scanner and a Vevo Visualsonix 2100 ultrasonic scanner. The results show improvements in sample processing time, accuracy and inter- and intra-operational variability. The sensitivity and accuracy of the microscopic identification system was of 97% and 92% respectively. The exactitude and precision error was up to 2% and the sensitivity and specificity went as high as 71% in the 3D reconstruction and texture analysis respectively.

This paper presents a landmark-free approach to estimate the fiberscope pose during endoscopic exploration for in-vivo optical biopsy. The fiberscope pose is estimated by fitting the projection of a virtual 3D cylinder into the endoscopic images. The cylinder axis is estimated based on the apparent contours using Plucker coordinates and its insertion is estimated by maximizing the similarity between binary masks. The performance of the method is evaluated on simulations: the mean Euclidian distance of fiberscopic tip between estimated pose and ground truth is 0.158 ± 0.113 mm. The in-vivo performance is assessed in two endoscopic sequences by comparing automatic RCF and manual segmentations in terms of angular deviation of the axis and Euclidian distance between the tip location. The estimation of the relative position of both cameras allows to perform registration between the two image modalities.

Pneumonic lung sonograms are known to include vertical comet-tail artifacts called B-lines. In this study, the potential of histogram properties from lung ultrasound images for the automatic identification of B-line artifacts is explored. Five histogram features (skewness, kurtosis, standard deviation, energy and average) were calculated for intercostal spaces. The sample consisted of 15 positive- and 15 negative-diagnosed B-mode videos selected by a medical expert and captured in a local pediatric health institute. For each frame, an initial domain of interest (DOI) starting from the pleural line is automatically outlined. The pleura is detected by a brightness based thresholding. Smaller regions containing the intercostal spaces inside the DOI are then outlined and histogram features are estimated. The potential classification of properties was evaluated independently, in pairs and using the group of 5. For single feature analysis, the optimal threshold was selected based on ROC (receiver operator characteristic) curve. For studying features in pairs a support vector machine (SVM) analysis using a RBF kernel was performed. Finally, for studying the five features, PCA (principal component analysis) was useful to determine the two principal components and apply an algorithm able to identify a B-line in the intercostal space. The results revealed that energy performed best as discriminator when using a single feature with 77% sensitivity, 75% specificity and 75% accuracy. When using features in pairs, average and skewness performed best with 93% sensitivity, 86% specificity and 88% accuracy. Finally, analyzing the 5 features, the results were 100% sensitivity, 98% specificity and 98% accuracy.

Cavity volume is an important clinical index for the assessment of the healing process and effectiveness of treatment applied on chronic ulcers. Recently, 3D scanners have proven to effectively track ulcer’s volume evolution. However, photogrammetry presents itself as a low cost and portable alternative. We conducted an inter-laboratory comparative study between photogrammetric and 3D scanner-based volume estimation of small skin ulcers. A total of 20 Cutaneous Leishmaniasis ulcers’ virtual models were generated using a commercial laser scanner and a full-HD portable camera. The reconstruction from videos was performed using comercial and open-source software (i.e., Agisoft Photoscan and VisualSFM). The results revealed similar performance with a median deviation of 16.18% and 21.10% (compared to 3DScan-based volume estimation) using VisualSFM and PhotoScan respectively. In addition, both methods proved similar efficiency in the assessment of healing ulcers when compared to 3D-scanner.