Niveditha Kalavakonda

Bio: Niveditha Kalavakonda is an academic researcher from University of Washington. The author has contributed to research in topics: Computer science & Augmented reality. The author has an hindex of 4, co-authored 7 publications receiving 95 citations. Previous affiliations of Niveditha Kalavakonda include Indian Institute of Technology Madras.

2017 Robotic Instrument Segmentation Challenge.

Abstract: In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.

Learned Hand Gesture Classification Through Synthetically Generated Training Samples

Abstract: Hand gestures are a natural component of human-human communication. Simple hand gestures are intuitive and can exhibit great lexical variety. It stands to reason that such a user input mechanism can have many benefits, including seamless interaction, intuitive control and robustness to physical constraints and ambient electrical, light and sound interference. However, while semantic and logical information encoded via hand gestures is readily decoded by humans, leveraging this communication channel in human-machine interfaces remains a challenge. Recent data-driven deep learning approaches are promising towards uncovering abstract and complex relationships that manual and direct rule-based classification schemes fail to discover. Such an approach is amenable towards hand gesture recognition, but requires myriad data which can be collected physically via user experiments. This process, however, is onerous and tedious. A streamlined approach with less overhead is sought. To that end, this work presents a novel method of synthetic hand gesture dataset generation that leverages modern gaming engines. Furthermore, preliminary results indicate that the dataset, despite being synthetic and requiring no physical data collection, is both accurate and rich enough to train a real-world hand gesture classifier that operates in real-time.

Augmented Reality Application for Aiding Tumor Resection in Skull-Base Surgery

Abstract: Treatment of cancer patients has improved with advances in tumor resection techniques for skull-base surgery. However, a secondary procedure or chemotherapy is often required to treat residual tumor to prevent recurrence. With the advent of assistive technology, such as augmented reality, a myriad of possibilities have been facilitated for the field of surgery. This work explores the development of an augmented reality application to improving the tumor margin excised during surgical procedures, with a focus on skull-base surgery. An isosurface reconstruction algorithm was integrated with the Microsoft HoloLens, a self-contained holographic computer, to enable visualization of Computed Tomography (CT) imaging superimposed in 3D on the patient. The results suggest that though the device has limitations at its current stage, the Microsoft HoloLens could be used for planning and overlaying the imaging information on the patient for removal of lesions in real-time. The modules developed could also be extended to other types of surgery involving visualization of Digital Imaging and Communication in Medicine (DICOM) files.

Autonomous Neurosurgical Instrument Segmentation Using End-To-End Learning

Abstract: Monitoring surgical instruments is an essential task in computer-assisted interventions and surgical robotics. It is also important for navigation, data analysis, skill assessment and surgical workflow analysis in conventional surgery. However, there are no standard datasets and benchmarks for tool identification in neurosurgery. To this end, we are releasing a novel neurosurgical instrument segmentation dataset called NeuroID for advancing research in the field. Delineating surgical tools from the background requires accurate pixel-wise instrument segmentation. In this paper, we present a comparison between three encoder-decoder approaches to binary segmentation of neurosurgical instruments, where we classify each pixel in the image to be either tool or background. A baseline performance was obtained by using heuristics to combine extracted features. We also extend the analysis to a publicly available robotic instrument segmentation dataset and include its results. The source code for our methods and the neurosurgical instrument dataset will be made publicly available (http://brl.ee.washington.edu/robotics/surgical-robotics/neurosurgical-instrument-segmentation) to facilitate reproducibility.

Therapeutic genome editing: prospects and challenges

Abstract: Recent advances in the development of genome editing technologies based on programmable nucleases have substantially improved our ability to make precise changes in the genomes of eukaryotic cells. Genome editing is already broadening our ability to elucidate the contribution of genetics to disease by facilitating the creation of more accurate cellular and animal models of pathological processes. A particularly tantalizing application of programmable nucleases is the potential to directly correct genetic mutations in affected tissues and cells to treat diseases that are refractory to traditional therapies. Here we discuss current progress toward developing programmable nuclease–based therapies as well as future prospects and challenges.

Deep Learning Based Robotic Tool Detection and Articulation Estimation With Spatio-Temporal Layers

Abstract: Surgical-tool joint detection from laparoscopic images is an important but challenging task in computer-assisted minimally invasive surgery. Illumination levels, variations in background and the different number of tools in the field of view, all pose difficulties to algorithm and model training. Yet, such challenges could be potentially tackled by exploiting the temporal information in laparoscopic videos to avoid per frame handling of the problem. In this letter, we propose a novel encoder–decoder architecture for surgical instrument joint detection and localization that uses three-dimensional convolutional layers to exploit spatio-temporal features from laparoscopic videos. When tested on benchmark and custom-built datasets, a median Dice similarity coefficient of 85.1% with an interquartile range of 4.6% highlights performance better than the state of the art based on single-frame processing. Alongside novelty of the network architecture, the idea for inclusion of temporal information appears to be particularly useful when processing images with unseen backgrounds during the training phase, which indicates that spatio-temporal features for joint detection help to generalize the solution.

CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

Abstract: Data-driven computational approaches have evolved to enable extraction of information from medical images with reliability, accuracy, and speed, which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theaters are extremely complex and typically rely on poorly integrated intraoperative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer-assisted interventions, we highlight the crucial need to take the context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer-assisted intervention (CAI4CAI) arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors, and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human–AI actor team; and how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision-making ultimately producing more precise and reliable interventions.

A review on the applications of virtual reality, augmented reality and mixed reality in surgical simulation: an extension to different kinds of surgery.

Abstract: Background: Research proves that the apprenticeship model, which is the gold standard for training surgical residents, is obsolete. For that reason, there is a continuing effort toward the developm...

2018 Robotic Scene Segmentation Challenge

Abstract: In 2015 we began a sub-challenge at the EndoVis workshop at MICCAI in Munich using endoscope images of ex-vivo tissue with automatically generated annotations from robot forward kinematics and instrument CAD models. However, the limited background variation and simple motion rendered the dataset uninformative in learning about which techniques would be suitable for segmentation in real surgery. In 2017, at the same workshop in Quebec we introduced the robotic instrument segmentation dataset with 10 teams participating in the challenge to perform binary, articulating parts and type segmentation of da Vinci instruments. This challenge included realistic instrument motion and more complex porcine tissue as background and was widely addressed with modifications on U-Nets and other popular CNN architectures. In 2018 we added to the complexity by introducing a set of anatomical objects and medical devices to the segmented classes. To avoid over-complicating the challenge, we continued with porcine data which is dramatically simpler than human tissue due to the lack of fatty tissue occluding many organs.