Trushali Doshi

Semi-automatic segmentation of tongue tumors from magnetic resonance imaging

Radiation therapy is one of the most effective modalities for treatment of tongue cancer. In order to optimize radiation dose to the tumor region, it is necessary to segment the tumor from normal region. This paper presents a new semiautomatic algorithm that is demonstrated to be able to segment tongue tumor from gadolinium-enhanced T1-weighted magnetic resonance imaging (MRI) to support radiation planning. This algorithm takes sequential MRI slices with visible tongue tumor. The Tumors region from each slice is segmented using three steps (i) preprocessing, (ii) initialization and (iii) localized region-based level set segmentation. The segmentation results obtained from proposed algorithm are compared with manual segmentation from clinical expert. Results from 9 MRI slices show that there is a good overlap between semi-automatic and manual segmentation results with dice similarity coefficient (DSC) of 0.87±0.05.

Automatic pharynx and larynx cancer segmentation framework (PLCSF) on contrast enhanced MR images

A novel and effective pharynx and larynx cancer segmentation framework (PLCSF) is presented for automatic base of tongue and larynx cancer segmentation from gadolinium-enhanced T1-weighted magnetic resonance images (MRI). The aim of the proposed PLCSF is to assist clinicians in radiotherapy treatment planning. The initial processing of MRI data in PLCSF includes cropping of region of interest; reduction of artefacts and detection of the throat region for the location prior. Further, modified fuzzy c-means clustering is developed to robustly separate candidate cancer pixels from other tissue types. In addition, region-based level set method is evolved to ensure spatial smoothness for the final segmentation boundary after noise removal using non-linear and morphological filtering. Validation study of PLCSF on 102 axial MRI slices demonstrate mean dice similarity coefficient of 0.79 and mean modified Hausdorff distance of 2.2 mm when compared with manual segmentations. Comparison of PLCSF with other algorithms validates the robustness of the PLCSF. Inter- and intra-variability calculations from manual segmentations suggest that PLCSF can help to reduce the human subjectivity.

3-dimensional throat region segmentation from MRI data based on fourier interpolation and 3-dimensional level set methods

A new algorithm for 3D throat region segmentation from magnetic resonance imaging (MRI) is presented. The proposed algorithm initially pre-processes the MRI data to increase the contrast between the throat region and its surrounding tissues and to reduce artifacts. Isotropic 3D volume is reconstructed using the Fourier interpolation. Furthermore, a cube encompassing the throat region is evolved using level set method to form a smooth 3D boundary of the throat region. The results of the proposed algorithm on real and synthetic MRI data are used to validate the robustness and accuracy of the algorithm.

A Novel Decentralised System Architecture for Multi-camera Target Tracking

Target tracking in a multi-camera system is an active and challenging research that in many systems requires video synchronisation and knowledge of the camera set-up and layout. In this paper a highly flexible, modular and decentralised system architecture is presented for multi-camera target tracking with relaxed synchronisation constraints among camera views. Moreover, the system does not rely on positional information to handle camera hand-off events. As a practical application, the system itself can, at any time, automatically select the best target view available, to implicitly solve occlusion. Further, to validate the proposed architecture, an extension to a multi-camera environment of the colour-based IMS-SWAD tracker is used. The experimental results show that the tracker can successfully track a chosen target in multiple views, in both indoor and outdoor environments, with non-overlapping and overlapping camera views.

OC-0563: MRI based 3D reconstruction of pharyngeal cancer to aid clinical oncologists in radiotherapy treatment planning

OC-0563 MRI based 3D reconstruction of pharyngeal cancer to aid clinical oncologists in radiotherapy treatment planning T. Doshi, D. Grose, K. MacKenzie, C. Wilson, J. Soraghan, L. Petropoulakis, G. Di Caterina University of Strathclyde, Department of Electronic & Electrical Engineering, Glasgow, United Kingdom Gartnavel General Hospital, Beatson West of Scotland Cancer Centre, Glasgow, United Kingdom NHS Greater Glasgow and Clyde, Royal Infirmary, Glasgow, United Kingdom

Automatic detection of larynx cancer from contrast-enhanced magnetic resonance images

Detection of larynx cancer from medical imaging is important for the quantification and for the definition of target volumes in radiotherapy treatment planning (RTP). Magnetic resonance imaging (MRI) is being increasingly used in RTP due to its high resolution and excellent soft tissue contrast. Manually detecting larynx cancer from sequential MRI is time consuming and subjective. The large diversity of cancer in terms of geometry, non-distinct boundaries combined with the presence of normal anatomical regions close to the cancer regions necessitates the development of automatic and robust algorithms for this task. A new automatic algorithm for the detection of larynx cancer from 2D gadoliniumenhanced T1-weighted (T1+Gd) MRI to assist clinicians in RTP is presented. The algorithm employs edge detection using spatial neighborhood information of pixels and incorporates this information in a fuzzy c-means clustering process to robustly separate different tissues types. Furthermore, it utilizes the information of the expected cancerous location for cancer regions labeling. Comparison of this automatic detection system with manual clinical detection on real T1+Gd axial MRI slices of 2 patients (24 MRI slices) with visible larynx cancer yields an average dice similarity coefficient of 0.78±0.04 and average root mean square error of 1.82±0.28 mm. Preliminary results show that this fully automatic system can assist clinicians in RTP by obtaining quantifiable and non-subjective repeatable detection results in a particular time-efficient and unbiased fashion.