Representation learning is a critical task for medical image analysis in computer-aided diagnosis. However, it is challenging to learn discriminative features due to the limited size of the data set and the lack of labels. In this article, we propose a deep graph-based multimodal feature embedding (DGMFE) framework for medical image retrieval with application to breast tissue classification by learning discriminative features of probe-based confocal laser endomicroscopy (pCLE). We first build a multimodality graph model based on the visual similarity between pCLE data and reference histology images. The latent similar pCLE-histology pairs are extracted by walking with the cyclic path on the graph while the dissimilar pairs are extracted based on the geodesic distance. Given the similar and dissimilar pairs, the latent feature space is discovered by reconstructing the similarity between pCLE and histology images via deep Siamese neural networks. The proposed method is evaluated on a clinical database with 700 pCLE mosaics. The accuracy of image retrieval demonstrates that DGMFE can outperform previous works on feature learning. Especially, the top-1 accuracy in an eight-class retrieval task is 0.739, thus demonstrating a 10% improvement compared to the state-of-the-art method.

Robotic bronchoscopic intervention requires detailed 3D airway maps for both localisation and enhanced visualisation, especially at peripheral airways. Patient-specific airway maps can be generated from preoperative chest CT scans. Due to pathological abnormalities and anatomical variations, automatically delineating the airway tree with distal branches is a challenging task. In the paper, we propose a cascaded 2D+3D model that has been tailored for airway segmentation from pathological CT scans. A novel 2D neural network is developed to generate the initial predictions where the peripheral airways are refined by a 3D adversarial training model. A sampling strategy based on a sequence of morphological operations is employed for the concatenation of the 2D and 3D models. The method has been validated on 20 pathological CT scans with results demonstrating improved segmentation accuracy and consistency, especially in peripheral airways.

Bronchoscopic intervention, as a minimally invasive method for the diagnosis and treatment of lung diseases, has attracted more and more attention in recent years. However, existing endobronchial instruments lack the steerability accessing the peripheral airways with difficult bifurcations. This paper presents a novel wire-driven dexterous manipulator for the guidance of such instruments. Precision laser profiling is used to cut a stainless steel tube into multiple interlocked segments with revolute joints. The outer diameter of the manipulator is 2.20 mm which is small enough to be inserted into the working channels of most commercial bronchoscopes and distal airways, while keeping a large inner lumen with a diameter of 1.44 mm for passing various bronchoscopic instruments. The small bending radius provides enough flexibility to navigate inside the complex bronchial tree. Two kinematic models are proposed to predict the manipulator configuration from the translation of actuation wires. The former model is geometrically derived with the assumption of constant curvature bending and the latter one is statistically driven by capturing the motion trajectories of manipulator joints. A prototype of our low-cost add-on instrument guidance robot for bronchoscopic intervention is presented which can be easily integrated into current clinical routine.