Seed point discontinuity-based segmentation method for the substantia nigra and the red nucleus in quantitative susceptibility maps.

BACKGROUND: The automatic segmentation of cerebral nuclei in the quantitative susceptibility mapping (QSM) images can provide assistance for surgical treatment and pathological mechanism studies. However, as the most frequently used segmentation method, the atlas method provides unsatisfactory results when segmenting the substantia nigra (SN) and the red nucleus (RN). PURPOSE: To propose and evaluate an improved automatic method based on seed points-discontinuity for segmentations of the SN and the RN in QSM images. STUDY TYPE: Prospective. SUBJECTS: In all, 22 subjects, 11 patients with Parkinson's disease (PD), and 11 healthy subjects (mean age of 68.0 ± 6.9 years) underwent MR scans. FIELD STRENGTH/SEQUENCE: 3T system and a 3D multiecho gradient echo sequence with monopolar readout gradient. ASSESSMENT: Manual segmentations by two radiologists (both with over 10 years of experience in neuroimaging) were used to establish a baseline for assessment. The Dice coefficient and the center-of-gravity distance was employed to evaluate the segmentation accuracy. STATISTICAL TESTS: The mean value and standard deviation of the Dice coefficient and center-of-gravity distance were calculated separately to compare segmentation results from the proposed method, the level set method, the atlas method (including the single-atlas method and the multi-atlas majority voting method). RESULTS: The statistical results of Dice coefficient of the SN and the RN between the ground truth and the segmentation were 0.79 ± 0.14 and 0.77 ± 0.06 for the proposed method, 0.40 ± 0.10 and 0.65 ± 0.09 for the level set method, 0.68 ± 0.09 and 0.64 ± 0.07 for the single-atlas method, 0.70 ± 0.06 and 0.68 ± 0.05 for the multi-atlas majority voting method, respectively. The proposed method also provides the lowest center-of-gravity distance value (1.05 ± 0.71 for the SN and 0.74 ± 0.35 for the RN). DATA CONCLUSION: The segmentation results of the proposed method performed well on the in vivo data and were closer to the manual segmentation than the atlas method. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;48:1112-1119.

SAB Net: A Semantic Attention Boosting Framework for Semantic Segmentation.

Semantic segmentation has achieved great progress by effectively fusing features of contextual information. In this article, we propose an end-to-end semantic attention boosting (SAB) framework to adaptively fuse the contextual information iteratively across layers with semantic regularization. Specifically, we first propose a pixelwise semantic attention (SAP) block, with a semantic metric representing the pixelwise category relationship, to aggregate the nonlocal contextual information. In addition, we improve the computation complexity of SAP block from O(n²) to O(n) for images with size n. Second, we present a categorywise semantic attention (SAC) block to adaptively balance the nonlocal contextual dependencies and the local consistency with a categorywise weight, overcoming the contextual information confusion caused by the feature imbalance within intra-category. Furthermore, we propose the SAB module to refine the segmentation with SAC and SAP blocks. By applying the SAB module iteratively across layers, our model shrinks the semantic gap and enhances the structure reasoning by fully utilizing the coarse segmentation information. Extensive quantitative evaluations demonstrate that our method significantly improves the segmentation results and achieves superior performance on the PASCAL VOC 2012, Cityscapes, PASCAL Context, and ADE20K datasets.

CAB U-Net: An end-to-end category attention boosting algorithm for segmentation.

With the development of machine learning and artificial intelligence, many convolutional neural networks (CNNs) based segmentation methods have been proposed for 3D cardiac segmentation. In this paper, we propose the category attention boosting (CAB) module, which combines the deep network calculation graph with the boosting method. On the one hand, we add the attention mechanism into the gradient boosting process, which enhances the information of coarse segmentation without high computation cost. On the other hand, we introduce the CAB module into the 3D U-Net segmentation network and construct a new multi-scale boosting model CAB U-Net which strengthens the gradient flow in the network and makes full use of the low resolution feature information. Thanks to the advantage that end-to-end networks can adaptively adjust the internal parameters, CAB U-Net can make full use of the complementary effects among different base learners. Extensive experiments on public datasets show that our approach can achieve superior performance over the state-of-the-art methods.

Combined use of choledochoscope and duodenoscope in treatment of bile peritonitis after removal of T-tube.

BACKGROUND: Biliary leakage after removal of a T-tube has significant morbidity and mortality. Its etiology is multifactorial. The treatment and outcome of this complication vary. In the present study we evaluated the procedures and efficacy of combined use of choledochoscope and duodenoscope in the treatment of bile peritonitis after T-tube removal. METHODS: The procedures and results of 11 cases of biliary leakage after removal of T-tube who had been treated from January 1998 to June 2004 by combined use of choledochoscope and duodenoscope were analyzed retrospectively. RESULT: After the treatment, 9 patients were cured, and 2 were reoperated on and cured. CONCLUSIONS: Biliary leakage after removal of T-tube can be cured successfully by combined use of choledochoscope and duodenoscope. Importantly, the method is simple, effective and safe, and mostly reoperation can be avoided.

A variational approach for pan-sharpening.

Pan-sharpening is a process of acquiring a high resolution multispectral (MS) image by combining a low resolution MS image with a corresponding high resolution panchromatic (PAN) image. In this paper, we propose a new variational pan-sharpening method based on three basic assumptions: 1) the gradient of PAN image could be a linear combination of those of the pan-sharpened image bands; 2) the upsampled low resolution MS image could be a degraded form of the pan-sharpened image; and 3) the gradient in the spectrum direction of pan-sharpened image should be approximated to those of the upsampled low resolution MS image. An energy functional, whose minimizer is related to the best pan-sharpened result, is built based on these assumptions. We discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. To verify the effectiveness of our method, we qualitatively and quantitatively compare it with some state-of-the-art schemes using QuickBird and IKONOS data. Particularly, we classify the existing quantitative measures into four categories and choose two representatives in each category for more reasonable quantitative evaluation. The results demonstrate the effectiveness and stability of our method in terms of the related evaluation benchmarks. Besides, the computation efficiency comparison with other variational methods also shows that our method is remarkable.