Classification of interstitial lung disease patterns using local DCT features and random forest.

Over the last decade, a plethora of computer-aided diagnosis (CAD) systems have been proposed aiming to improve the accuracy of the physicians in the diagnosis of interstitial lung diseases (ILD). In this study, we propose a scheme for the classification of HRCT image patches with ILD abnormalities as a basic component towards the quantification of the various ILD patterns in the lung. The feature extraction method relies on local spectral analysis using a DCT-based filter bank. After convolving the image with the filter bank, q-quantiles are computed for describing the distribution of local frequencies that characterize image texture. Then, the gray-level histogram values of the original image are added forming the final feature vector. The classification of the already described patches is done by a random forest (RF) classifier. The experimental results prove the superior performance and efficiency of the proposed approach compared against the state-of-the-art.

Feature-invariant image registration method for quantification of surgical outcomes in patients with craniosynostosis: a preliminary study

Craniosynostosis consists of a premature fusion of the sutures in an infant skull that restricts skull and brain growth. During the last decades, there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumference, and intracranial volume. However, these variables have failed in describing the local deformations and morphological changes that may have a role in the neurologic disorders observed in the patients. This report describes a rigid image registration-based method to evaluate outcomes of craniosynostosis surgical treatments, local quantification of head growth, and indirect intracranial volume change measurements. The developed semiautomatic analysis method was applied to computed tomography data sets of a 5-month-old boy with sagittal craniosynostosis who underwent expansion of the posterior skull with cranioplasty. Quantification of the local changes between pre- and postoperative images was quantified by mapping the minimum distance of individual points from the preoperative to the postoperative surface meshes, and indirect intracranial volume changes were estimated. The proposed methodology can provide the surgeon a tool for the quantitative evaluation of surgical procedures and detection of abnormalities of the infant skull and its development.

Computer-based summative assessment of clinical reasoning in clerkships: A mixed-method comparison of key feature problems with case-based multiple choice questions

Background: It is yet unclear if there are differences between using electronic key feature problems (KFPs) or electronic case-based multiple choice questions (cbMCQ) for the assessment of clinical decision making. Summary of Work: Fifth year medical students were exposed to clerkships which ended with a summative exam. Assessment of knowledge per exam was done by 6-9 KFPs, 9-20 cbMCQ and 9-28 MC questions. Each KFP consisted of a case vignette and three key features (KF) using “long menu” as question format. We sought students’ perceptions of the KFPs and cbMCQs in focus groups (n of students=39). Furthermore statistical data of 11 exams (n of students=377) concerning the KFPs and (cb)MCQs were compared. Summary of Results: The analysis of the focus groups resulted in four themes reflecting students’ perceptions of KFPs and their comparison with (cb)MCQ: KFPs were perceived as (i) more realistic, (ii) more difficult, (iii) more motivating for the intense study of clinical reasoning than (cb)MCQ and (iv) showed an overall good acceptance when some preconditions are taken into account. The statistical analysis revealed that there was no difference in difficulty; however KFP showed a higher discrimination and reliability (G-coefficient) even when corrected for testing times. Correlation of the different exam parts was intermediate. Conclusions: Students perceived the KFPs as more motivating for the study of clinical reasoning. Statistically KFPs showed a higher discrimination and higher reliability than cbMCQs. Take-home messages: Including KFPs with long menu questions into summative clerkship exams seems to offer positive educational effects.

Computerbasierte Prüfung von klinischem Denken in der klinischen Ausbildung: Ein Mixed-Method Vergleich von Key Feature Problemen mit fallbasierten Multiple Choice Typ A Fragen

Fragestellung/Einleitung: Es ist unklar inwiefern Unterschiede bestehen im Einsatz von Key Feature Problemen (KFP) mit Long Menu Fragen und fallbasierten Typ A Fragen (FTA) für die Überprüfung des klinischen Denkens (Clinical Reasoning) in der klinischen Ausbildung von Medizinstudierenden. Methoden: Medizinstudierende des fünften Studienjahres nahmen an ihrer klinischen Pädiatrie-Rotation teil, die mit einer summativen Prüfung endete. Die Überprüfung des Wissen wurde pro Prüfung elektronisch mit 6-9 KFP [1], [3], 9-20 FTA und 9-28 nichtfallbasierten Multiple Choice Fragen (NFTA) durchgeführt. Jedes KFP bestand aus einer Fallvignette und drei Key Features und nutzen ein sog. Long Menu [4] als Antwortformat. Wir untersuchten die Perzeption der KFP und FTA in Focus Gruppen [2] (n of students=39). Weiterhin wurden die statistischen Kennwerte der KFP und FTA von 11 Prüfungen (n of students=377) verglichen. Ergebnisse: Die Analyse der Fokusgruppen resultierte in vier Themen, die die Perzeption der KFP und deren Vergleich mit FTA darstellten: KFP wurden als 1. realistischer, 2. schwerer, und 3. motivierender für das intensive Selbststudium des klinischen Denkens als FTA aufgenommen und zeigten 4. insgesamt eine gute Akzeptanz sofern gewisse Voraussetzungen berücksichtigt werden. Die statistische Auswertung zeigte keinen Unterschied im Schwierigkeitsgrad; jedoch zeigten die KFP eine höhere Diskrimination und Reliabilität (G-coefficient) selbst wenn für die Prüfungszeit korrigiert wurde. Die Korrelation der verschiedenen Prüfungsteile war mittel. Diskussion/Schlussfolgerung: Die Studierenden erfuhren die KFP als motivierenden für das Selbststudium des klinischen Denkens. Statistisch zeigten die KFP eine grössere Diskrimination und höhere Relibilität als die FTA. Der Einbezug von KFP mit Long Menu in Prüfungen des klinischen Studienabschnitts erscheint vielversprechend und einen „educational effect“ zu haben.

Robust Denoising using Feature and Color Information

We propose a method that robustly combines color and feature buffers to denoise Monte Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or depth, are effective in determining denoising filters because features are highly correlated with rendered images. Filters based solely on features, however, are prone to blurring image details that are not well represented by the features. On the other hand, color buffers represent all details, but they may be less effective to determine filters because they are contaminated by the noise that is supposed to be removed. We propose to obtain filters using a combination of color and feature buffers in an NL-means and cross-bilateral filtering framework. We determine a robust weighting of colors and features using a SURE-based error estimate. We show significant improvements in subjective and quantitative errors compared to the previous state-of-the-art. We also demonstrate adaptive sampling and space-time filtering for animations.

Synchronization of two independently moving cameras without feature correspondences

In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and/or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach.