Physician-Friendly Tool Center Point Calibration Method for Robot-Assisted Puncture Surgery.

After each robot end tool replacement, tool center point (TCP) calibration must be performed to achieve precise control of the end tool. This process is also essential for robot-assisted puncture surgery. The purpose of this article is to solve the problems of poor accuracy stability and strong operational dependence in traditional TCP calibration methods and to propose a TCP calibration method that is more suitable for a physician. This paper designs a special binocular vision system and proposes a vision-based TCP calibration algorithm that simultaneously identifies tool center point position (TCPP) and tool center point frame (TCPF). An accuracy test experiment proves that the designed special binocular system has a positioning accuracy of ±0.05 mm. Experimental research shows that the magnitude of the robot configuration set is a key factor affecting the accuracy of TCPP. Accuracy of TCPF is not sensitive to the robot configuration set. Comparison experiments show that the proposed TCP calibration method reduces the time consumption by 82%, improves the accuracy of TCPP by 65% and improves the accuracy of TCPF by 52% compared to the traditional method. Therefore, the method proposed in this article has higher accuracy, better stability, less time consumption and less dependence on the operations than traditional methods, which has a positive effect on the clinical application of high-precision robot-assisted puncture surgery.

A method for quantitative analysis of clump thickness in cervical cytology slides.

Knowledge of the spatial distribution and thickness of cytology specimens is critical to the development of digital slide acquisition techniques that minimise both scan times and image file size. In this paper, we evaluate a novel method to achieve this goal utilising an exhaustive high-resolution scan, an over-complete wavelet transform across multi-focal planes and a clump segmentation of all cellular materials on the slide. The method is demonstrated with a quantitative analysis of ten normal, but difficult to scan Pap stained, Thin-prep, cervical cytology slides. We show that with this method the top and bottom of the specimen can be estimated to an accuracy of 1 µm in 88% and 97% of the fields of view respectively. Overall, cellular material can be over 30 µm thick and the distribution of cells is skewed towards the cover-slip (top of the slide). However, the median clump thickness is 10 µm and only 31% of clumps contain more than three nuclei. Therefore, by finding a focal map of the specimen the number of 1 µm spaced focal planes that are required to be scanned to acquire 95% of the in-focus material can be reduced from 25.4 to 21.4 on average. In addition, we show that by considering the thickness of the specimen, an improved focal map can be produced which further reduces the required number of 1 µm spaced focal planes to 18.6. This has the potential to reduce scan times and raw image data by over 25%.

A two-stage method to correct aberrations induced by slide slant in bright-field microscopy.

To achieve optimal image quality in bright field microscopy, the slide surface should be perpendicular to the optical axis of the microscope. However, in the recently proposed "slanted scan" slide acquisition technique, scan speed is increased by purposely slanting the slide by a small angle (of 3-5°) so that multiple focal depths can be imaged simultaneously. In this case, the slanted slide introduces a bend in the point spread function (PSF), resulting in a coma and other aberrations that degrade image quality. In this paper, we propose a two-stage deconvolution method specifically designed to correct the aberrations induced by a slanted scan, but with general applicability to high-resolution bright-field microscopy. Specifically, we initially apply phase deconvolution to correct the dominating coma aberration, before applying a conventional semi-blind deconvolution method to further improve image resolution and contrast. We also propose a novel method to estimate the degree of coma aberration and the PSF of the optics utilising actual cytology specimens. The efficacy of the proposed algorithm is demonstrated quantitatively on simulated data, against a ground-truth (object) image, and qualitatively on cervical cytology specimens. Results demonstrate both improved convergence speed of the two-stage approach, especially when correcting the bend in the PSF, and a resultant image quality that is comparable to a conventionally (flat) scanned specimen.

An algorithm for microscopic specimen delineation and focus candidate selection.

In this paper, we compare four field-of-view (FOV) metrics that, when applied to a low-resolution image of a microscope slide, are capable of both accurately delineating the specimen and selecting a subset of focus candidate FOVs required for construction of high-resolution focus map. The metrics evaluated are: threshold index (TI) that measures image intensity; normalised auto-correlation index (NACI) that measures spatial image similarity; auto-phase correlation index (APCI) that measures image phase diversity; and entropy index (EI) that measures the predictability of image intensities. Experiments are undertaken on a data set of forty slides including PAP stained Thin-prep cervical cytology and breast fine-needle aspiration slides and haematoxylin and eosin (HE) stained histology slides. These slides were scanned on an automated bright-field microscope and chosen to be indicative of a variety pathology specimens, containing artefacts such as excess coverslip glue and ink markers. Results are presented on the performance of each metric for correct ranking/segmentation of foreground (specimen) from background, and subsequently selecting focus candidate FOVs characteristic of the specimen's focal plane(s). The experimental results demonstrate that while NACI, APCI and EI are all effective at specimen delineation, only APCI is capable of effectively selecting superior focus candidates and ignoring artefacts.