Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) canbe unified under a single generalization of assigning a label, from an predefinedset, to each pixel in an image. We first formalize this generalizationand then present two probabilistic models capable of solving it. The firstmodel is based on independent pixel labeling using maximum a-posteriori(MAP) estimation. The second model is based on conditional randomfields (CRFs), where dependencies between pixels are defined using agraph structure. Furthermore, we demonstrate how supervised learningand an appropriate training set can be used to automatically determineall model parameters. We evaluate both models\u27 ability to segment achallenging dataset consisting of 116 images and compare our results to5 previously published methods

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) can be unified under a single generalization of assigning a label, from an predefined set, to each pixel in an image. We first formalize this generalization and then present two probabilistic models capable of solving it. The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. The second model is based on conditional random fields (CRFs), where dependencies between pixels are defined using a graph structure. Furthermore, we demonstrate how supervised learning and an appropriate training set can be used to automatically determine all model parameters. We evaluate both models' ability to segment a challenging dataset consisting of 116 images and compare our results to 5 previously published methods

Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI

ObjectiveWe demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging.MethodsPET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability.ResultsFor both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC.ConclusionThe first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity.SignificanceAs simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Towards automated skin lesion diagnosis

Melanoma, the deadliest form of skin cancer, must be diagnosed early in order to be treated effectively. Automated Skin Lesion Diagnosis (ASLD) attempts to accomplish this using digital dermoscopic images. This thesis investigates several areas in which ASLD can be improved. Typically, the ASLD pipeline consists of 5 stages: 1) image acquisition, 2) artifact detection, 3) lesion segmentation, 4) feature extraction and 5) classification. The main focus of the thesis is the development of two probabilistic models which are sufficiently general to perform several key tasks in the ASLD pipeline, including: artifact detection, lesion segmentation and feature extraction. We then show how all parameters of these two models can be inferred automatically using supervised learning and a set of examples. Additionally, we present methods to: 1) evaluate the experts’ perception of texture in images of dermoscopic skin lesions, 2) calibrate acquired digital dermoscopy images for color, lighting and chromatic aberration, and 3) digitally remove detected occluding artifacts. Our general probabilistic models’ ability to detect occluding hair and segment lesions performs comparably to other, less general, methods. Perceptually, we conclude that the textural information in skin lesions exists independently of color. Calibrating, for colour and lighting, we achieve results consistent with previous work; calibrating for chromatic aberration, we are able to reduce distortions by 47%. Furthermore, our method to digitally remove occluding artifacts outperforms previous work

Contour correspondence via ant colony optimization. volume 0

We formulate contour correspondence as a Quadratic Assignment Problem (QAP), incorporating proximity information. By maintaining the neighborhood relation between points this way, we show that better matching results are obtained in practice. We propose the first Ant Colony Optimization (ACO) algorithm specifically aimed at solving the QAP-based shape correspondence problem. Our ACO framework is flexible in the sense that it can handle general point correspondence, but also allows extensions, such as order preservation, for the more specialized contour matching problem. Various experiments are presented which demonstrate that this approach yields highquality correspondence results and is computationally efficient when compared to other methods. 1