Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained $0.29$–$0.36$ fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between $0.01$ and $0.05$ on average and the spectral angle between $1.5$ and $3.2$ degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.

In many cases, chemotherapeutic anticancer therapies’ contribution to life extension is low, and serious adverse effects are common.^1,2 Also the cost of cancer treatments is extremely high and is increasing constantly.²

<!-- *** Custom HTML *** --> Corrections to the article, “Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks,” by S. Rahkonen et al. This article [J. Med. Imag. 7(2), 024001 (2020) doi: 10.1117/1.JMI.7.2.024001] was originally published online on 7 April 2020 with errors in the panel labels for Fig. 6. The following corrections were made to the originally published version: <!-- named anchor --> 1. in Fig 6(h), “Prediction (O)” was corrected to read “Prediction (S)”; 2. in Fig 6(i), “Prediction (I)” was corrected to read “Prediction (IP)”; 3. in Fig 6(j), “Errors (R)” was corrected to read “Errors (P)”; 4. in Fig 6(k), “Errors (O)” was corrected to read “Errors (S)”; 5. in Fig 6(l), “Errors (I)” was corrected to read “Errors (IP)”; 6. in Fig 6(m), “Abs. errors (R)” was corrected to read “Abs. errors (P)”; 7. in Fig 6(n), “Abs. errors (O)” was corrected to read “Abs. errors (S)”; and 8. in Fig 6(o), “Abs. errors (I)” was corrected to read “Abs. errors (IP).” This article was corrected on 13 April 2020.