SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction

• URL: http://arxiv.org/abs/2012.11779v1
• Date: Tue, 22 Dec 2020 01:28:30 GMT
• Title: SERV-CT: A disparity dataset from CT for validation of endoscopic 3D reconstruction
• Authors: P.J. "Eddie'' Edwards, Dimitris Psychogyios, Stefanie Speidel, Lena Maier-Hein and Danail Stoyanov
• Abstract summary: We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT) The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images.
• Score: 8.448866668577946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In computer vision, reference datasets have been highly successful in promoting algorithmic development in stereo reconstruction. Surgical scenes gives rise to specific problems, including the lack of clear corner features, highly specular surfaces and the presence of blood and smoke. Publicly available datasets have been produced using CT and either phantom images or biological tissue samples covering a relatively small region of the endoscope field-of-view. We present a stereo-endoscopic reconstruction validation dataset based on CT (SERV-CT). Two {\it ex vivo} small porcine full torso cadavers were placed within the view of the endoscope with both the endoscope and target anatomy visible in the CT scan. Orientation of the endoscope was manually aligned to the stereoscopic view. Reference disparities and occlusions were calculated for 8 stereo pairs from each sample. For the second sample an RGB surface was acquired to aid alignment of smooth, featureless surfaces. Repeated manual alignments showed an RMS disparity accuracy of ~2 pixels and a depth accuracy of ~2mm. The reference dataset includes endoscope image pairs with corresponding calibration, disparities, depths and occlusions covering the majority of the endoscopic image and a range of tissue types. Smooth specular surfaces and images with significant variation of depth are included. We assessed the performance of various stereo algorithms from online available repositories. There is a significant variation between algorithms, highlighting some of the challenges of surgical endoscopic images. The SERV-CT dataset provides an easy to use stereoscopic validation for surgical applications with smooth reference disparities and depths with coverage over the majority of the endoscopic images. This complements existing resources well and we hope will aid the development of surgical endoscopic anatomical reconstruction algorithms.

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