The length and thickness of the uterus and endometrium are morphology characteristics as important measures for uterine diagnosis. In diagnosing uterine, doctors mark anatomical landmark points of uterus and endometrium in order to measure their length and thickness. However, it is difficult to reliably detect the landmarks of the uterus and endometrium due to the ambiguous boundaries and heterogeneous textures of uterus transvaginal ultrasound image. In this paper, we propose a novel region-guided adversarial learning framework for anatomical landmark detection in transvaginal ultrasound image, aiming at automatically detecting the landmark points of uterus and endometrium of transvaginal ultrasound image to a diagnostical precision. In the proposed adversarial learning scheme, the proposed framework consists of a landmark predictor and two discriminators for the uterus and endometrium. The proposed landmark predictor is to detect the desired landmarks of both uterus and endometrium regions from transvaginal ultrasound image. The discriminator is to determine whether the predicted landmarks of uterus and endometrium are related with their regions or not (i.e., whether the predicted landmark points are on the region boundaries or not.). By adversarial learning between the predictor and the discriminators with uterus and endometrium region images, the performance of the landmark predictor can be improved. In testing, with the trained predictor only, uterus and endometrium landmarks are predicted. Experimental results demonstrated that the proposed method achieved a high accuracy in detecting landmarks of the uterus and endometrium in the ultrasound image.
In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has been successfully applied to medical image analysis including CADx. Existing deep learning based CADx approaches, however, have a limitation in explaining the diagnostic decision. In real clinical practice, clinical decisions could be made with reasonable explanation. So current deep learning approaches in CADx are limited in real world deployment. In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework. The proposed framework is devised with a generative adversarial network, which consists of interpretable diagnosis network and synthetic lesion generative network to learn the relationship between malignancy and a standardized description (BI-RADS). The lesion generative network and the interpretable diagnosis network compete in an adversarial learning so that the two networks are improved. The effectiveness of the proposed method was validated on public mammogram database. Experimental results showed that the proposed ICADx framework could provide the interpretability of mass as well as mass classification. It was mainly attributed to the fact that the proposed method was effectively trained to find the relationship between malignancy and interpretations via the adversarial learning. These results imply that the proposed ICADx framework could be a promising approach to develop the CADx system.
KEYWORDS: Holography, Fringe analysis, Digital holography, 3D image reconstruction, Computer generated holography, Holograms, Near field diffraction, Data analysis, 3D image processing, Image quality, Visualization, Reconstruction algorithms, Optimization (mathematics)
In this paper, we investigate quality factors of numerical reconstruction with a small number of signals based on sparsity from a holographic fringe pattern. Holographic fringe pattern generated by Fresnel diffraction is a complex amplitude and sparse distribution in frequency domain. The sparsity of holographic fringe pattern could play a key role in reconstruction quality assessment in compressive holography. In this paper we have investigated sparsity constraint on holographic fringe pattern which influences the overall quality of numerically reconstructed data. In addition, we have investigated reconstruction quality for various subsampling methods including uniform sampling, random sampling, variable density sampling, and magnitude-based sampling. Experiments have been conducted to evaluate reconstruction qualities on sparsity constraints and sampling patterns. Experimental results indicate that the way to extract the sparse signals could significantly affect the quality of the numerical reconstruction in digital holography and visually plausible reconstruction could be obtained with a sparse holographic fringe pattern.
KEYWORDS: Computer generated holography, Holography, Fourier transforms, 3D displays, Fringe analysis, Near field diffraction, Visualization, Spatial resolution, Ray tracing
Computer generated hologram (CGH) is becoming increasingly important for a 3-D display in various applications including virtual reality. In the CGH, holographic fringe patterns are generated by numerically calculating them on computer simulation systems. However, a heavy computational cost is required to calculate the complex amplitude on CGH plane for all points of 3D objects. This paper proposes a new fast CGH generation based on the sparsity of CGH for 3D point cloud model. The aim of the proposed method is to significantly reduce computational complexity while maintaining the quality of the holographic fringe patterns. To that end, we present a new layer-based approach for calculating the complex amplitude distribution on the CGH plane by using sparse FFT (sFFT). We observe the CGH of a layer of 3D objects is sparse so that dominant CGH is rapidly generated from a small set of signals by sFFT. Experimental results have shown that the proposed method is one order of magnitude faster than recently reported fast CGH generation.
This paper presents a new multi-view stereo image synthesis using binocular symmetric hole filling. In autostereoscopic displays, multi-view synthesis is needed to provide multiple perspectives of the same scene, as viewed from multiple viewing positions. In the warped image at a distant virtual viewpoint, it is difficult to generate visually plausible multi-view stereo images in multi-view synthesis since very large hole regions (i.e., disoccluded regions) could be induced. Also, binocular asymmetry between the synthesized left-eye and right-eye images is one of the critical factors, which leads to a visual discomfort in stereoscopic viewing. In this paper, we maintain the binocular symmetry using the already filled regions in an adjacent view. The proposed method introduces a binocular symmetric hole filling based on the global optimization for binocular symmetry in the synthesized multi-view stereo images. The experimental results showed that the proposed method outperformed those of the existing methods.
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