Purpose This paper introduces the SciKit-Surgery libraries, designed to enable rapid development of clinical applications for image-guided interventions. SciKit-Surgery implements a family of compact, orthogonal, libraries accompanied by robust testing, documentation, and quality control. SciKit-Surgery libraries can be rapidly assembled into testable clinical applications and subsequently translated to production software without the need for software reimplementation. The aim is to support translation from single surgeon trials to multicentre trials in under 2 years. Methods At the time of publication, there were 13 SciKit-Surgery libraries provide functionality for visualisation and augmented reality in surgery, together with hardware interfaces for video, tracking, and ultrasound sources. The libraries are stand-alone, open source, and provide Python interfaces. This design approach enables fast development of robust applications and subsequent translation. The paper compares the libraries with existing platforms and uses two example applications to show how SciKit-Surgery libraries can be used in practice. Results Using the number of lines of code and the occurrence of cross-dependencies as proxy measurements of code complexity, two example applications using SciKit-Surgery libraries are analysed. The SciKit-Surgery libraries demonstrate ability to support rapid development of testable clinical applications. By maintaining stricter orthogonality between libraries, the number, and complexity of dependencies can be reduced. The SciKit-Surgery libraries also demonstrate the potential to support wider dissemination of novel research. Conclusion The SciKit-Surgery libraries utilise the modularity of the Python language and the standard data types of the NumPy package to provide an easy-to-use, well-tested, and extensible set of tools for the development of applications for image-guided interventions. The example application built on SciKit-Surgery has a simpler dependency structure than the same application built using a monolithic platform, making ongoing clinical translation more feasible.

Abstract-There are several automated random strategies of software testing based on the presence of point, block and strip failure domains inside the whole input domain. As yet no particular, fully automated test strategy has been developed for the discovery and evaluation of the failure domains. We therefore have developed Automated Discovery of Failure Domain (ADFD), a new random test strategy that finds the failures and their domains in a given system under test. It further provides visualization of the identified pass and fail domains. In this paper we describe ADFD strategy, its implementation in York Extensible Testing Infrastructure (YETI) and illustrate its working with the help of an example. We report on experiments in which we tested error-seeded one and two-dimensional numerical programs. Our experimental results show that for each program, ADFD strategy successfully performs identification of failures, failure domains and their representation on graphical chart.

SnappySonic provides an ultrasound acquisition replay simulator designed for public engagement and training. It provides a simple interface to allow users to experience ultrasound acquisition without the need for specialist hardware or acoustically compatible phantoms. The software is implemented in Python, built on top of a set of open source Python modules targeted at surgical innovation. The library has high potential for reuse, most obviously for those who want to simulate ultrasound acquisition, but it could also be used as a user interface for displaying high dimensional images or video data.

Abstract-The paper presents a new and improved automated random testing technique named as Dirt Spot Sweeping Random (DSSR) strategy based on the rationale that, "when failures lies in the contiguous locations across the input domain, the effectiveness of random testing can be further improved through diversity of test cases". The DSSR strategy selects neighboring values for the subsequent tests on identification of failure. Resultantly, selected values sweep around the failure leading to the discovery of new failures in the vicinity. To evaluate the effectiveness of DSSR strategy a total of 60 classes (35,785 lines of code), each class with 30 x 105 calls, were tested by Random (R), Random+ (R+) and DSSR strategies. T-Test analysis showed significantly better performance of DSSR compared to R strategy in 17 classes and R+ strategy in 9 classes. In the remaining classes all the three strategies performed equally well. Numerically, the DSSR strategy found 43 and 12 more unique failures than R and R+ strategies respectively. This study comprehends that DSSR strategy will have a profound positive impact on the failurefinding ability of R and R+ testing.