Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment

VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided. The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the “Beeldenstorm” collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called “Finding Related Resources Across Languages,” involved linking video to material on the same subject in a different language. Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language “Beeldenstorm” collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names

The effects of computed tomography image characteristics and knot spacing on the spatial accuracy of B-spline deformable image registration in the head and neck geometry

Objectives: To explore the effects of computed tomography (CT) image characteristics and B-spline knot spacing (BKS) on the spatial accuracy of a B-spline deformable image registration (DIR) in the head-and-neck geometry. Methods: The effect of image feature content, image contrast, noise, and BKS on the spatial accuracy of a B-spline DIR was studied. Phantom images were created with varying feature content and varying contrast-to-noise ratio (CNR), and deformed using a known smooth B-spline deformation. Subsequently, the deformed images were repeatedly registered with the original images using different BKSs. The quality of the DIR was expressed as the mean residual displacement (MRD) between the known imposed deformation and the result of the B-spline DIR. Finally, for three patients, head-and-neck planning CT scans were deformed with a realistic deformation field derived from a rescan CT of the same patient, resulting in a simulated deformed image and an a-priori known deformation field. Hence, a B-spline DIR was performed between the simulated image and the planning CT at different BKSs. Similar to the phantom cases, the DIR accuracy was evaluated by means of MRD. Results: In total, 162 phantom registrations were performed with varying CNR and BKSs. MRD-values = +/- 250 HU and noise <+/- 200 HU. Decreasing the image feature content resulted in increased MRD-values at all BKSs. Using BKS = 15 mm for the three clinical cases resulted in an average MRD <1.0 mm. Conclusions: For synthetically generated phantoms and three real CT cases the highest DIR accuracy was obtained for a BKS between 10-20 mm. The accuracy decreased with decreasing image feature content, decreasing image contrast, and higher noise levels. Our results indicate that DIR accuracy in clinical CT images (typical noise levels <+/- 100 HU) will not be effected by the amount of image noise

Reproducibility of the lung anatomy under Active Breathing Coordinator control: Dosimetric consequences for scanned proton treatments.

Purpose/Objective The treatment of moving targets with scanning proton beams is challenging. By controlling lung volumes, Active Breathing Control (ABC) assists breath-holding for motion mitigation. The delivery of proton treatment fractions often exceeds feasible breath-hold durations, requiring high breath-hold reproducibility. Therefore, we investigated dosimetric consequences of anatomical reproducibility uncertainties in the lung under ABC, evaluating robustness of scanned proton treatments during breath-hold. Material/Methods T1-weighted MRIs of five volunteers were acquired during ABC, simulating image acquisition during four subsequent breath-holds within one treatment fraction. Deformation vector fields obtained from these MRIs were used to deform 95% inspiration phase CTs of 3 randomly selected non-small-cell lung cancer patients (Figure 1). Per patient, an intensity-modulated proton plan was recalculated on the 3 deformed CTs, to assess the dosimetric influence of anatomical breath-hold inconsistencies. Results Dosimetric consequences were negligible for patient 1 and 2 (Figure 1). Patient 3 showed a decreased volume (95.2%) receiving 95% of the prescribed dose for one deformed CT. The volume receiving 105% of the prescribed dose increased from 0.0% to 9.9%. Furthermore, the heart volume receiving 5 Gy varied by 2.3%. Figure 2 shows dose volume histograms for all relevant structures in patient 3. Conclusion Based on the studied patients, our findings suggest that variations in breath-hold have limited effect on the dose distribution for most lung patients. However, for one patient, a significant decrease in target coverage was found for one of the deformed CTs. Therefore, further investigation of dosimetric consequences from intra-fractional breath-hold uncertainties in the lung under ABC is needed