Fully automatic lesion boundary detection in ultrasound breast images

We propose a novel approach to fully automatic lesion boundary detection in ultrasound breast images. The novelty of the proposed work lies in the complete automation of the manual process of initial Region-of-Interest (ROI) labeling and in the procedure adopted for the subsequent lesion boundary detection. Histogram equalization is initially used to preprocess the images followed by hybrid filtering and multifractal analysis stages. Subsequently, a single valued thresholding segmentation stage and a rule-based approach is used for the identification of the lesion ROI and the point of interest that is used as the seed-point. Next, starting from this point an Isotropic Gaussian function is applied on the inverted, original ultrasound image. The lesion area is then separated from the background by a thresholding segmentation stage and the initial boundary is detected via edge detection. Finally to further improve and refine the initial boundary, we make use of a state-of-the-art active contour method (i.e. gradient vector flow (GVF) snake model). We provide results that include judgments from expert radiologists on 360 ultrasound images proving that the final boundary detected by the proposed method is highly accurate. We compare the proposed method with two existing stateof- the-art methods, namely the radial gradient index filtering (RGI) technique of Drukker et. al. and the local mean technique proposed by Yap et. al., in proving the proposed method’s robustness and accuracy

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Stent implant follow-up in intravascular optical coherence tomography images

The objectives of this article are (i) to utilize computer methods in detection of stent struts imaged in vivo by optical coherence tomography (OCT) during percutaneous coronary interventions (PCI); (ii) to provide measurements for the assessment and monitoring of in-stent restenosis by OCT post PCI. Thirty-nine OCT cross-sections from seven pullbacks from seven patients presenting varying degrees of neointimal hyperplasia (NIH) are selected, and stent struts are detected. Stent and lumen boundaries are reconstructed and one experienced observer analyzed the strut detection, the lumen and stent area measurements, as well as the NIH thickness in comparison to manual tracing using the reviewing software provided by the OCT manufacturer (LightLab Imaging, MA, USA). Very good agreements were found between the computer methods and the expert evaluations for lumen cross-section area (mean difference = 0.11 ± 0.70 mm2; r2 = 0.98, P\ 0.0001) and the stent cross-section area (mean difference = 0.10 ± 1.28 mm2; r2 = 0.85, P value\ 0.0001). The average number of detected struts was 10.4 ± 2.9 per crosssection when the expert identified 10.5 ± 2.8 (r2 = 0.78, P value\0.0001). For the given patient dataset: lumen cross-sectional area was on the average (6.05 ± 1.87 mm2), stent cross-sectional area was (6.26 ± 1.63 mm2), maximum angle between struts was on the average (85.96 ± 54.23), maximum, average, and minimum distance between the stent and the lumen were (0.18 ± 0.13 mm), (0.08 ± 0.06 mm), and (0.01 ± 0.02 mm), respectively, and stent eccentricity was (0.80 ± 0.08). Low variability between the expert and automatic method was observed in the computations of the most important parameters assessing the degree of neointimal tissue growth in stents imaged by OCT pullbacks. After further extensive validation, the presented methods might offer a robust automated tool that will improve the evaluation and follow-up monitoring of in-stent restenosis in patients