Mass segmentation using a combined method for cancer detection

<p>Abstract</p> <p>Background</p> <p>Breast cancer is one of the leading causes of cancer death for women all over the world and mammography is thought of as one of the main tools for early detection of breast cancer. In order to detect the breast cancer, computer aided technology has been introduced. In computer aided cancer detection, the detection and segmentation of mass are very important. The shape of mass can be used as one of the factors to determine whether the mass is malignant or benign. However, many of the current methods are semi-automatic. In this paper, we investigate fully automatic segmentation method.</p> <p>Results</p> <p>In this paper, a new mass segmentation algorithm is proposed. In the proposed algorithm, a fully automatic marker-controlled watershed transform is proposed to segment the mass region roughly, and then a level set is used to refine the segmentation. For over-segmentation caused by watershed, we also investigated different noise reduction technologies. Images from DDSM were used in the experiments and the results show that the new algorithm can improve the accuracy of mass segmentation.</p> <p>Conclusions</p> <p>The new algorithm combines the advantages of both methods. The combination of the watershed based segmentation and level set method can improve the efficiency of the segmentation. Besides, the introduction of noise reduction technologies can reduce over-segmentation.</p

A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images

<p>Abstract</p> <p>Background</p> <p>Speckles in ultrasound imaging affect image quality and can make the post-processing difficult. Speckle reduction technologies have been employed for removing speckles for some time. One of the effective speckle reduction technologies is anisotropic diffusion. Anisotropic diffusion technology can remove the speckles effectively while preserving the edges of the image and thus has drawn great attention from image processing scientists. However, the proposed methods in the past have different disadvantages, such as being sensitive to the number of iterations or low capability of preserving the details of the ultrasound images. Thus a detail preserved anisotropic diffusion speckle reduction with less sensitive to the number of iterations is needed. This paper aims to develop this kind of technologies.</p> <p>Results</p> <p>In this paper, we propose a robust detail preserving anisotropic diffusion filter (RDPAD) for speckle reduction. In order to get robust diffusion, the proposed method integrates Tukey error norm function into the detail preserving anisotropic diffusion filter (DPAD) developed recently. The proposed method could prohibit over-diffusion and thus is less sensitive to the number of iterations</p> <p>Conclusions</p> <p>The proposed anisotropic diffusion can preserve the important structure information of the original image while reducing speckles. It is also less sensitive to the number of iterations. Experimental results on real ultrasound images show the effectiveness of the proposed anisotropic diffusion filter.</p

Computational design of steady 3D dissection puzzles

Dissection puzzles require assembling a common set of pieces into multiple distinct forms. Existing works focus on creating 2D dissection puzzles that form primitive or naturalistic shapes. Unlike 2D dissection puzzles that could be supported on a tabletop surface, 3D dissection puzzles are preferable to be steady by themselves for each assembly form. In this work, we aim at computationally designing steady 3D dissection puzzles. We address this challenging problem with three key contributions. First, we take two voxelized shapes as inputs and dissect them into a common set of puzzle pieces, during which we allow slightly modifying the input shapes, preferably on their internal volume, to preserve the external appearance. Second, we formulate a formal model of generalized interlocking for connecting pieces into a steady assembly using both their geometric arrangements and friction. Third, we modify the geometry of each dissected puzzle piece based on the formal model such that each assembly form is steady accordingly. We demonstrate the effectiveness of our approach on a wide variety of shapes, compare it with the state-of-the-art on 2D and 3D examples, and fabricate some of our designed puzzles to validate their steadiness

Integrative metabolomic and transcriptomic analysis reveals difference in glucose and lipid metabolism in the longissimus muscle of Luchuan and Duroc pigs

Luchuan pig, an obese indigenous Chinese porcine breed, has a desirable meat quality and reproductive capacity. Duroc, a traditional western breed, shows a faster growth rate, high feed efficiency and high lean meat rate. Given the unique features these two porcine breeds have, it is of interest to investigate the underlying molecular mechanisms behind their distinctive nature. In this study, the metabolic and transcriptomic profiles of longissimus dorsi muscle from Duroc and Luchuan pigs were compared. A total of 609 metabolites were identified, 77 of which were significantly decreased in Luchuan compared to Duroc, and 71 of which were significantly elevated. Most differentially accumulated metabolites (DAMs) upregulated in Luchuan were glycerophospholipids, fatty acids, oxidized lipids, alcohols, and amines, while metabolites downregulated in Luchuan were mostly amino acids, organic acids and nucleic acids, bile acids and hormones. From our RNA-sequencing (RNA-seq) data we identified a total of 3638 differentially expressed genes (DEGs), 1802 upregulated and 1836 downregulated in Luchuan skeletal muscle compared to Duroc. Combined multivariate and pathway enrichment analyses of metabolome and transcriptome results revealed that many of the DEGs and DAMs are associated with critical energy metabolic pathways, especially those related to glucose and lipid metabolism. We examined the expression of important DEGs in two pathways, AMP-activated protein kinase (AMPK) signaling pathway and fructose and mannose metabolism, using Real-Time Quantitative Reverse Transcription PCR (qRT-PCR). Genes related to glucose uptake, glycolysis, glycogen synthesis, fatty acid synthesis (PFKFB1, PFKFB4, MPI, TPI1, GYS1, SLC2A4, FASN, IRS1, ULK1) are more activated in Luchuan, while genes related to fatty acid oxidation, cholesterol synthesis (CPT1A, HMGCR, FOXO3) are more suppressed. Energy utilization can be a decisive factor to the distinctive metabolic, physiological and nutritional characteristics in skeletal muscle of the two breeds we studied. Our research may facilitate future porcine breeding projects and can be used to reveal the potential molecular basis of differences in complex traits between various breeds

RESA: Recurrent Feature-Shift Aggregator for Lane Detection

Lane detection is one of the most important tasks in self-driving. Due to various complex scenarios (e.g., severe occlusion, ambiguous lanes, etc.) and the sparse supervisory signals inherent in lane annotations, lane detection task is still challenging. Thus, it is difficult for the ordinary convolutional neural network (CNN) to train in general scenes to catch subtle lane feature from the raw image. In this paper, we present a novel module named REcurrent Feature-Shift Aggregator (RESA) to enrich lane feature after preliminary feature extraction with an ordinary CNN. RESA takes advantage of strong shape priors of lanes and captures spatial relationships of pixels across rows and columns. It shifts sliced feature map recurrently in vertical and horizontal directions and enables each pixel to gather global information. RESA can conjecture lanes accurately in challenging scenarios with weak appearance clues by aggregating sliced feature map. Moreover, we propose a Bilateral Up-Sampling Decoder that combines coarse-grained and fine-detailed features in the up-sampling stage. It can recover the low-resolution feature map into pixel-wise prediction meticulously. Our method achieves state-of-the-art results on two popular lane detection benchmarks (CULane and Tusimple). Code has been made available at: https://github.com/ZJULearning/resa