Non-learning Stereo-aided Depth Completion under Mis-projection via Selective Stereo Matching

We propose a non-learning depth completion method for a sparse depth map captured using a light detection and ranging (LiDAR) sensor guided by a pair of stereo images. Generally, conventional stereo-aided depth completion methods have two limiations. (i) They assume the given sparse depth map is accurately aligned to the input image, whereas the alignment is difficult to achieve in practice. (ii) They have limited accuracy in the long range because the depth is estimated by pixel disparity. To solve the abovementioned limitations, we propose selective stereo matching (SSM) that searches the most appropriate depth value for each image pixel from its neighborly projected LiDAR points based on an energy minimization framework. This depth selection approach can handle any type of mis-projection. Moreover, SSM has an advantage in terms of long-range depth accuracy because it directly uses the LiDAR measurement rather than the depth acquired from the stereo. SSM is a discrete process; thus, we apply variational smoothing with binary anisotropic diffusion tensor (B-ADT) to generate a continuous depth map while preserving depth discontinuity across object boundaries. Experimentally, compared with the previous state-of-the-art stereo-aided depth completion, the proposed method reduced the mean absolute error (MAE) of the depth estimation to 0.65 times and demonstrated approximately twice more accurate estimation in the long range. Moreover, under various LiDAR-camera calibration errors, the proposed method reduced the depth estimation MAE to 0.34-0.93 times from previous depth completion methods.Comment: 15 pages, 13 figure

Re-biopsy status among non-small cell lung cancer patients in Japan: A retrospective study

AbstractObjectiveDisease progression because of acquired resistance is common in advanced or metastatic epidermal growth factor receptor (EGFR)-mutation positive non-small cell lung cancer (NSCLC), despite initial response to EGFR-tyrosine kinase inhibitors (TKIs). In Japan, transbronchial tissue biopsy is the most common sampling method used for re-biopsy to identify patients eligible for treatment. We aimed to investigate the success rate of re-biopsy and re-biopsy status of patients with advanced or metastatic NSCLC completing first-line EGFR-TKI therapy.Patients and methodsThis was a retrospective, multi-center, Japanese study. The target patients in the study were EGFR mutation-positive NSCLC patients. The primary endpoint was the success rate (number of cases in which tumor cells were detected/total number of re-biopsies performed×100). Secondary endpoints included differences between the status of the first biopsy and that of the re-biopsy in the same patient population, and the details of cases in which re-biopsy could not be carried out. Re-biopsy-associated complications were also assessed.ResultsOverall, 395 patients were evaluated (median age 63 years), with adenocarcinoma being the most common tumor type. Re-biopsy was successful in 314 patients (79.5%). Compared with the sampling method at first biopsy, at re-biopsy, the surgical resection rate increased from 1.8% to 7.8%, and percutaneous tissue biopsy increased from 7.6% to 29.1%, suggesting the difficulty of performing re-biopsy. Approximately half of the patients had T790M mutations, which involved a Del19 mutation in 55.6% of patients and an L858R mutation in 43.0%. Twenty-three patients (5.8%) had re-biopsy- associated complications, most commonly pneumothorax.ConclusionsSuccess rate for re-biopsy in this study was approximately 80%. Our study sheds light on the re-biopsy status after disease progression in patients with advanced or metastatic NSCLC. This information is important to improve the selection of patients who may benefit from third-generation TKIs

Integration and Visualization of Mineralogical and Topographical Information Derived from ASTER and DEM Data

This paper proposes a method of combining and visualizing multiple lithological indices derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and topographical information derived from digital elevation model (DEM) data in a single color image that can be easily interpreted from a geological point of view. For the purposes of mapping silicate rocks, carbonate rocks, and clay minerals in hydrothermal alteration zones, two new indices derived from ASTER thermal infrared emissivity data were developed to identify silicate rocks, and existing indices were adopted to indicate the distribution of carbonate rocks and the species and amounts of clay mineral. In addition, another new method was developed to visualize the topography from DEM data. The lithological indices and topographical information were integrated using the hue–saturation–value (HSV) color model. The resultant integrated image was evaluated by field survey and through comparison with the results of previous studies in the Cuprite and Goldfield areas, Nevada, USA. It was confirmed that the proposed method can be used to visualize geological information and that the resulting images can easily be interpreted from a geological point of view