Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show great potential for automatic image segmentation, but these techniques usually require a huge amount of accurately annotated reference segmentations for training. The guiding hypothesis of this paper was that crowd-algorithm collaboration could evolve as a key technique in large-scale medical data annotation. As an initial step toward this goal, we evaluated the performance of untrained individuals to detect and correct errors made by three-dimensional (3-D) medical segmentation algorithms. To this end, we developed a multistage segmentation pipeline incorporating a hybrid crowd-algorithm 3-D segmentation algorithm integrated into a medical imaging platform. In a pilot study of liver segmentation using a publicly available dataset of computed tomography scans, we show that the crowd is able to detect and refine inaccurate organ contours with a quality similar to that of experts (engineers with domain knowledge, medical students, and radiologists). Although the crowds need significantly more time for the annotation of a slice, the annotation rate is extremely high. This could render crowdsourcing a key tool for cost-effective large-scale medical image annotation.
Proc. SPIE. 9789, Medical Imaging 2016: PACS and Imaging Informatics: Next Generation and Innovations
KEYWORDS: Data modeling, Data modeling, Surgery, Data storage, Image segmentation, Medical imaging, Data archive systems, Cognition, Neuroimaging, Data integration, Knowledge acquisition, Information science, Standards development, Knowledge management, Imaging informatics, Picture Archiving and Communication System
In the surgical domain, individual clinical experience, which is derived in large part from past clinical cases, plays
an important role in the treatment decision process. Simultaneously the surgeon has to keep track of a large
amount of clinical data, emerging from a number of heterogeneous systems during all phases of surgical treatment.
This is complemented with the constantly growing knowledge derived from clinical studies and literature. To
recall this vast amount of information at the right moment poses a growing challenge that should be supported
by adequate technology.
While many tools and projects aim at sharing or integrating data from various sources or even provide knowledge-based
decision support - to our knowledge - no concept has been proposed that addresses the entire surgical
pathway by accessing the entire information in order to provide context-aware cognitive assistance. Therefore a
semantic representation and central storage of data and knowledge is a fundamental requirement.
We present a semantic data infrastructure for integrating heterogeneous surgical data sources based on a common
knowledge representation. A combination of the Extensible Neuroimaging Archive Toolkit (XNAT) with semantic
web technologies, standardized interfaces and a common application platform enables applications to access and
semantically annotate data, perform semantic reasoning and eventually create individual context-aware surgical
The infrastructure meets the requirements of a cognitive surgical assistant system and has been successfully
applied in various use cases. The system is based completely on free technologies and is available to the community
as an open-source package.
Current fully automatic liver tumor segmentation systems are designed to work on a single CT-image. This hinders these systems from the detection of more complex types of liver tumor. We therefore present a new algorithm for liver tumor segmentation that allows incorporating different CT scans and requires no manual interaction. We derive a liver segmentation with state-of-the-art shape models which are robust to initialization. The tumor segmentation is then achieved by classifying all voxels into healthy or tumorous tissue using Extremely Randomized Trees with an auto-context learning scheme. Using DALSA enables us to learn from only sparse annotations and allows a fast set-up for new image settings. We validate the quality of our algorithm with exemplary segmentation results.
Radiotherapy is frequently used to treat unoperated or partially resected tumors. Tumor movement, e.g. caused by respiration, is a major challenge in this context. Markers can be implanted around the tumor prior to radiation therapy for accurate tracking of tumor movement. However, accurate placement of these markers while keeping a secure margin around the target and while taking into account critical structures is a difficult task. Computer-assisted needle insertion has been an active field of research in the past decades. However, the challenge of navigated marker placement for motion compensated radiotherapy has not yet been addressed. This work presents a system to support marker implantation for radiotherapy under consideration of safety margins and optimal marker configuration. It is designed to allow placement of markers both percutaneously and during an open liver surgery. To this end, we adapted the previously proposed <i>EchoTrack </i>system which integrates ultrasound (US) imaging and electromagnetic (EM) tracking in a single mobile modality. The potential of our new marker insertion concept was evaluated in a phantom study by inserting sets of three markers around dedicated targets (n=22) simultaneously spacing the markers evenly around the target as well as placing the markers in a defined distance to the target. In all cases the markers were successfully placed in a configuration fulfilling the predefined criteria. This includes a minimum distance of 18.9 ± 2.4 mm between marker and tumor as well as a divergence of 2.1 ± 1.5 mm from the planned marker positions. We conclude that our system has high potential to facilitate the placement of markers in suitable configurations for surgeons without extensive experience in needle punctions as high quality configurations were obtained even by medical non-experts.