Segmentation is a key step in analyzing and processing medical images. Due to the low fault tolerance in medical imaging, manual segmentation remains the de facto standard in this domain. Besides, efforts to automate the segmentation process often rely on large amounts of manually labeled data. While existing software supporting manual segmentation is rich in features and delivers accurate results, the necessary time to set it up and get comfortable using it can pose a hurdle for the collection of large datasets. This work introduces a client server based online environment, referred to as Studierfenster (this http URL), that can be used to perform manual segmentations directly in a web browser. The aim of providing this functionality in the form of a web application is to ease the collection of ground truth segmentation datasets. Providing a tool that is quickly accessible and usable on a broad range of devices, offers the potential to accelerate this process. The manual segmentation workflow of Studierfenster consists of dragging and dropping the input file into the browser window and slice-by-slice outlining the object under consideration. The final segmentation can then be exported as a file storing its contours and as a binary segmentation mask. In order to evaluate the usability of Studierfenster, a user study was performed. The user study resulted in a mean of 6.3 out of 7.0 possible points given by users, when asked about their overall impression of the tool. The evaluation also provides insights into the results achievable with the tool in practice, by presenting two ground truth segmentations performed by physicians.
´ There is a wide range of offline software tools available that offer sophisticated medical image analysis and processing capabilities, including tools for manual segmentation. Examples include 3D Slicer @cite_4 and MeVisLab @cite_10 , @cite_5 . The drawback of these tools is that they require a local installation and are thus not readily available on any device.
@cite_10 @cite_5 @cite_4
2912317562 1857578225 1123242753 2127890285
Surgical tool segmentation in endoscopic images is the first step towards pose estimation and (sub-)task automation in challenging minimally invasive surgical operations. While many approaches in the literature have shown great results using modern machine learning methods such as convolutional neural networks, the main bottleneck lies in the acquisition of a large number of manually-annotated images for efficient learning. This is especially true in surgical context, where patient-to-patient differences impede the overall generalizability. In order to cope with this lack of annotated data, we propose a self-supervised approach in a robot-assisted context. To our knowledge, the proposed approach is the first to make use of the kinematic model of the robot in order to generate training labels. The core contribution of the paper is to propose an optimization method to obtain good labels for training despite an unknown hand-eye calibration and an imprecise kinematic model. The labels can subsequently be used for fine-tuning a fully-convolutional neural network for pixel-wise classification. As a result, the tool can be segmented in the endoscopic images without needing a single manually-annotated image. Experimental results on phantom and in vivo datasets obtained using a flexible robotized endoscopy system are very promising. Processing and visualization of 3D medical data is nowadays a common problem. However, it remains challenging because the diversification and complexification of the available sources of information, as well as the specific requirements of clinicians, make it difficult to solve in a computer science point of view. Indeed, clinicians need ergonomic, efficient, intuitive and reactive softwares. Moreover, they need new solutions to fully exploit their data, but they often cannot access state-of-the-art methods as those are mostly available in complicated softwares. The MedINRIA software was born to fill this lack and consists of a collection of tools that optimally exploit various types of data (e.g., 3D images, diffusion tensor fields, neural fibers as obtained in DT-MRI). It provides state-of-the-art algorithms while keeping a user-friendly graph-ical interface. For each of these tools, we first introduce its dedicated application and the processing methods it contains. Then, we focus on the features that make interactions with data even more intuitive. Med-INRIA is a free software, available on Windows, Linux and MacOSX. Other MedINRIA tools are underway to make cutting edge research in medical imaging rapidly available to clinicians. The interest clinicians have shown in MedINRIA so far indicates that the need of such simple, yet powerful softwares is real and increasing. Automated left ventricle (LV) segmentation in 3D ultrasound (3D-US) remains a challenging research problem due to variable image quality and limited field-of-view. Modern segmentation approaches (shape, appearance and contour model based surface fitting) require an accurate initialization and good image boundary features to obtain reliable and consistent results. They are therefore not well suited for this problem. The proposed method overcomes those limitations with a novel and generic 3D-US image boundary representation technique: Probabilistic Edge Map (PEM). This new representation captures regularized and complete edge responses from standard 3D-US images. PEM is utilized in a multi-atlas LV segmentation framework to spatially align target and atlas images. Experiments on data from the MICCAI CETUS challenge show that the proposed approach is better suited for LV segmentation than the active contour, appearance and voxel classification approaches, achieving lower surface distance errors and better LV volume estimates. Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle