Jean-Baptiste Fasquel

An interactive medical image segmentation system based on the optimal management of regions of interest using topological medical knowledge

This paper presents an original interactive system for efficient medical image segmentation in computer aided diagnosis. The main originality concerns the method used to manage, according to an a priori topological-based structural model, regions of interest (ROIs) within which computations can be constrained. The goal is then to avoid the processing of irrelevant image points, therefore improving and accelerating segmentations. In the case of a hierarchical modeling procedure, our ROI management method enables, for delineating a given medical structure, to optimally determine image points of interest by taking previously segmented structures into account. We propose a mathematical formulation of the method as well as a possible implementation within an interactive system. We also detail an experience report focussing on the segmentation of several abdominal structures from a CT image. It illustrates the behavior and the potential of our method.

Virtual reality and augmented reality applied to laparoscopic and notes procedures

Computer-assisted surgery led to a major improvement in medicine. Such an improvement can be summarized in three major steps. The first one consists in an automated 3D modelling of patients from their medical images. The second one consists in using this modelling in surgical planning and simulator software offering then the opportunity to train the surgical gesture before carrying it out. The last step consists in intraoperatively superimposing preoperative data onto the real view of patients. This augmented reality provides surgeons a view in transparency of their patient allowing to track instruments and improve pathology targeting. We will present here our results in these different domains applied to laparoscopic and NOTES procedures.

A modular and evolutive component oriented software architecture for patient modeling

This paper deals with the design aspect of a software aiming at modeling the anatomical and pathological structures of patients from medical images, for diagnosis purposes. In terms of functionalities, it allows to combine image processing algorithms, and to visualize and manipulate 3D models and images. The proposed software uses specific extensible and reusable components and a system managing their combination, thanks to a formal XML-based description of their interfaces. This architecture facilitates the dynamic integration of new functionalities, in particular in terms of image processing algorithms. We describe the structural and behavioral aspects of the proposed reusable component-based architecture. We also discuss the potential of this work for developing other softwares in the field of computer aided surgery.

An efficient and generic extension to ITK to process arbitrary shaped regions of interest

The paper describes a software method to extend ITK (Insight ToolKit, supported by the National Library of Medicine), leading to ITK++. This method, which is based on the extension of the iterator design pattern, allows the processing of regions of interest with arbitrary shapes, without modifying the existing ITK code. We experimentally evaluate this work by considering the practical case of the liver vessel segmentation from CT-scan images, where it is pertinent to constrain processings to the liver area. Experimental results clearly prove the interest of this work: for instance, the anisotropic filtering of this area is performed in only 16 s with our proposed solution, while it takes 52 s using the native ITK framework. A major advantage of this method is that only add-ons are performed: this facilitates the further evaluation of ITK++ while preserving the native ITK framework.

Local-feature-based similarity measure for stochastic resonance in visual perception of spatially structured images.

For images, stochastic resonance or useful-noise effects have previously been assessed with low-level pixel-based information measures. Such measures are not sensitive to coherent spatial structures usually existing in images. As a result, we show that such measures are not sufficient to properly account for stochastic resonance occurring in visual perception. We introduce higher-level similarity measures, inspired from visual perception, and based on local feature descriptors of scale invariant feature transform (SIFT) type. We demonstrate that such SIFT-based measures allow for an assessment of stochastic resonance that matches the visual perception of images with spatial structures. Constructive action of noise is registered in this way with both additive noise and multiplicative speckle noise. Speckle noise, with its grainy appearance, is particularly prone to introducing spurious spatial structures in images, and the stochastic resonance visually perceived and quantitatively assessed with SIFT-based measures is specially examined in this context.

Improving genericity and performances of medical systems based on image analysis

This paper deals with the improvement of the genericity of the well-known ITK medical image processing library. Such a library is the core of any medical system/software based on the medical image analysis (e.g. computer aided diagnosis, surgery planning...). This proposed improvement consists in, without algorithm rewriting, extending ITK iterators (leading to an ITK++framework) in order to constrain algorithms to user-specified image areas. We experimentally evaluate this work by considering the practical case of liver vessel segmentation from CT-scan images, where it is pertinent to constrain processings to the liver area: this reduces the number of voxels to process. Experimental results clearly prove the interest of this work: for example, the anisotropic filtering of this area is performed in only 16 seconds with our proposed solution, while 52 seconds are required using the native limited ITK framework. Moreover, we also show that the code resulting from the proposed improvement remains easy to manage. A major advantage of the proposed solution is that the native ITK library is not modified because the improvement consists in some add-ons: this facilitates the further evaluation of the pertinence of the proposed design while preserving the native ITK framework.

An Aggregation Plateform for IoT-Based Healthcare: Illustration for Bioimpedancemetry, Temperature and Fatigue Level Monitoring

In this paper, we detail an in-home aggregation plateform for monitoring physiological parameters, and involving two objective physical sensors (bio-impedanceter and thermometer) and a subjective one (fatigue level perceived by the patient). This plateform uses modern IoT-related technologies such as embedded systems (Raspberry Pi and Arduino) and the MQTT communication protocol. Compared to many related works, monitoring is enterely achieved using a box as a central element, while the mobile device (tablet) is only used for controlling the acquisition procedure using a simple web browser, without any specific application. An example of a time stamped set of acquired data is shown, based on the in-home monitoring of healthy volunteers.

A semi-automated method for measuring the evolution of both lumen area and blood flow in carotid from Phase Contrast MRI

Phase-Contrast (PC) velocimetry Magnetic Resonance Imaging (MRI) is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels and the measurement of both lumen area and blood flow evolutions. In this paper, we propose a semi-automated method for extracting lumen boundaries of the carotid artery and compute both lumen area and blood flow evolutions over the cardiac cycle. This method uses narrow band region-based active contours in order to correctly capture the lumen boundary without being corrupted by surrounding structures. This approach is compared to traditional edge-based active contours, considered in related works, which significantly underestimate lumen area and blood flow. Experiments are performed using both a sequence of a homemade phantom and sequences of 20 real carotids, including a comparison with manual segmentation performed by a radiologist expert. Results obtained on the phantom sequence show that the edge-based approach leads to an underestimate of carotid lumen area and related flows of respectively 18.68% and 4.95%. This appears significantly larger than weak errors obtained using the region-based approach (respectively 2.73% and 1.23%). Benefits appear even better on the real sequences. The edge-based approach leads to underestimates of 40.88% for areas and 13.39% for blood flows, compared to limited errors of 7.41% and 4.6% with our method. Experiments also illustrate the high variability and therefore the lack of reliability of manual segmentation.

Region-based active contours for computer-aided analysis of carotid Phase Contrast MRI

Phase-Contrast (PC) velocimetry MRI is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels. In this paper, we evaluate the ability of region-based active contours to efficiently segment carotid vessels, compared to traditionnal edge-based active contours considered in related works. Experiments are performed using both a sequence of a homemade phantom and a sequence of a patient carotid which has been manually analyzed by a radiologist expert. Results obtained on phantom sequence show that the edge-based approach leads to an underestimate of carotid lumen area and related flows of respectively 18.4 % and 3.6 %. This appears significantly larger than weak errors obtained using the region-based approach (respectively 2.3 % and 0.7 %). Benefits appear even better on the patient sequence. The edge-based approach leads to underestimates of 20.5 % for areas and 13.8 % for blood flows, compared to limited errors of 5.2 % and 4 % with our proposal. This improvement is confirmed by similarity indices used to compare segmentations.

Coupling anatomical and functional information for the computer-aided delineation of Phase-Contrast MRI images using active contours

Phase-Contrast (PC) velocimetry MRI is a useful modality to explore cardiovascular pathologies, but requires the automatic segmentation of vessels. Most existing segmentation approaches focus on the exploitation of anatomical information to guide algorithms, such as widely used active contours we consider in this paper, although acquisitions simultaneously provide functional information. Due to noisy regions surrounding vessels, it is difficult to integrate this information as a complementary guiding force for attracting contours towards vessel boundaries. As we illustrate, the recently proposed phase quality map is interesting but appears not appropriately formulated to build useful attraction forces. In this paper, a refinement is presented to overcome this issue, together with preliminary experimental results. We detail and illustrate how this refinement operates on phase information, and how the coupling of both anatomical and functional information can improve the efficiency of active contours, compared to the only use of anatomical information.