Automatic initialisation of 3D deformable models for cartilage segmentation

Deformable models are a highly accurate and flexible approach to segmenting structures in medical images. The primary drawback of deformable models is that they are sensitive to initialisation, with accurate and robust results often requiring initialisation close to the true object in the image. Automatically obtaining a good initialisation is problematic for many structures in the body. The cartilages of the knee are a thin elastic material that cover the ends of the bone, absorbing shock and allowing smooth movement. The degeneration of these cartilages characterize the progression of osteoarthritis. The state of the art in the segmentation of the cartilage are 2D semi-automated algorithms. These algorithms require significant time and supervison by a clinical expert, so the development of an automatic segmentation algorithm for the cartilages is an important clinical goal. In this paper we present an approach towards this goal that allows us to automatically providing a good initialisation for deformable models of the patella cartilage, by utilising the strong spatial relationship of the cartilage to the underlying bone.

On sensor network segmentation for urban water distribution monitoring

Identifying water wastage in forms of leaks in a water distribution network of any city becomes essential as droughts are presenting serious threats to few major cities. In this paper, we propose a deployment of sensor network for monitoring water flow in any water distribution network. We cover the issues related with designing such a dedicated sensor network by considering types of sensors required, sensors' functionality, data collection, and providing computation serving as leak detection mechanism. The main focus of this paper is on appropriate network segmentation that provides the base for hierarchical approach to pipes' failure detection. We show a method for sensors allocation to the network in order to facilitate effective pipes monitoring. In general, the identified computational problem belongs to hard problems. The paper shows a heuristic method to build effective hierarchy of the network segmentation.

Automatic segmentation of the knee bones using 3D active shape models

This paper presents an automated segmentation approach for MR images of the knee bones. The bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. The segmentation is performed using 3D active shape models (ASM), which are initialized using an affine registration to an atlas. The 3D ASMs of the bones are created automatically using a point distribution model optimization scheme. The accuracy and robustness of the segmentation approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images.

Monitoring inflammation and airway remodeling by FMT in a chronic asthma model

Asthma is a multifactorial disease for which a variety of mouse models have been developed. A major drawback of these models is represented by the transient nature of the airway pathology peaking 24 to 72 hours after challenge and resolving in 1 to 2 weeks. The objective of this study is to characterize the temporal evolution of pulmonary inflammation and remodeling in a recently described mouse model of chronic asthma (8 week treatment with 3 allergens relevant for the human pathology: Dust mite, Ragweed, and Aspergillus; DRA). We studied the DRA model taking advantage of fluorescence molecular tomography (FMT) imaging using near-infrared probes to non-invasively evaluate lung inflammation and airway remodeling. At 4, 6, 8 or 11 weeks, cathepsin- and metalloproteinase-dependent fluorescence was evaluated in vivo. A subgroup of animals, after 4 weeks of DRA, was treated with Budesonide (100 µg/kg intranasally) daily for 4 weeks. Cathepsin-dependent fluorescence in DRA-sensitized mice resulted significantly increased at 6 and 8 weeks, and was markedly inhibited by budesonide. This fluorescent signal well correlated with ex vivo analysis such as bronchoalveolar lavage eosinophils and alveolar cell infiltration. Metalloproteinase-dependent fluorescence was significantly increased at 8 and 11 weeks, nicely correlated with collagen deposition, as evaluated histologically by Masson’s Trichrome staining, and airway epithelium hypertrophy, and was also partly inhibited by budesonide. In conclusion, FMT proved suitable for longitudinal study to evaluate asthma progression, both in terms of inflammatory cell infiltration and airway remodeling, allowing the determination of treatment efficacy in a chronic asthma model in mice.

Training Bayesian networks for image segmentation

We are concerned with the problem of image segmentation in which each pixel is assigned to one of a predefined finite number of classes. In Bayesian image analysis, this requires fusing together local predictions for the class labels with a prior model of segmentations. Markov Random Fields (MRFs) have been used to incorporate some of this prior knowledge, but this not entirely satisfactory as inference in MRFs is NP-hard. The multiscale quadtree model of Bouman and Shapiro (1994) is an attractive alternative, as this is a tree-structured belief network in which inference can be carried out in linear time (Pearl 1988). It is an hierarchical model where the bottom-level nodes are pixels, and higher levels correspond to downsampled versions of the image. The conditional-probability tables (CPTs) in the belief network encode the knowledge of how the levels interact. In this paper we discuss two methods of learning the CPTs given training data, using (a) maximum likelihood and the EM algorithm and (b) emphconditional maximum likelihood (CML). Segmentations obtained using networks trained by CML show a statistically-significant improvement in performance on synthetic images. We also demonstrate the methods on a real-world outdoor-scene segmentation task.

Dynamical local models for segmentation and prediction of financial time series

In the analysis and prediction of many real-world time series, the assumption of stationarity is not valid. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for financial markets. We introduce a new model which combines a dynamic switching (controlled by a hidden Markov model) and a non-linear dynamical system. We show how to train this hybrid model in a maximum likelihood approach and evaluate its performance on both synthetic and financial data.