ABSTRACT
Instrument pose estimation is a key demand in computer-aided surgery, and its main challenges lie in two aspects: Firstly, the difficulty of obtaining stable corresponding image feature points due to the instruments' high refraction and complicated background, and secondly, the lack of labeled pose data. This study aims to tackle the pose estimation problem of surgical instruments in the current endoscope system using a single endoscopic image. More specifically, a weakly supervised method based on the instrument's image segmentation contour is proposed, with the effective assistance of synthesized endoscopic images. Our method consists of the following three modules: a segmentation module to automatically detect the instrument in the input image, followed by a point inference module to predict the image locations of the implicit feature points of the instrument, and a point back-propagatable Perspective-n-Point module to estimate the pose from the tentative 2D-3D corresponding points. To alleviate the over-reliance on point correspondence accuracy, the local errors of feature point matching and the global inconsistency of the corresponding contours are simultaneously minimized. Our proposed method is validated with both real and synthetic images in comparison with the current state-of-the-art methods.
ABSTRACT
Cucumis melo L. var. Agrestis Naud chloroplast genome sequence was first reported. The size of the chloroplast genome is 156,016 bp in length, including a large single copy region (LSC) of 86,334 bp, a small single copy region (SSC) of 18,088 bp, and a pair of inverted repeat (IRa and IRb) regions with 25,797 bp. Cucumis melo L. var. Agrestis Naud chloroplast genome encodes 133 genes, including 88 mRNA genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis with the reported chloroplast sequences shows that Cucumis melo L. var. Agrestis Naud was closely related to Cucumis melo subsp. melo.
