- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multi-scale sifting for mammographic mass detection and segmentation
摘要： Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
关键词： Morphological sifting,Mammography,Breast mass detection and segmentation,Cascaded random forest,Ensemble learning
更新于2025-09-23 15:22:29
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Automatic Detection and Segmentation of Laser Stripes for Industrial Measurement
摘要： Laser stripe plays an important role in industrial vision measurement as the major auxiliary feature. Existing researches mainly focus on the application of small size parts. However, with the increase of field of view, it is difficult to extract laser stripes robustly in varying field measurement situations for the complex background, low proportion and uneven characteristic of laser stripes. To increase the measurement adaptability in complex environment, an automatic laser stripe detection and segmentation algorithm is proposed. First, the dataset is constructed by a large number of image patches collected in the field and laboratory, and laser stripe patches in the imbalanced dataset are expanded by data augmentation method. Next, the detection of the laser stripe is initially realized based on the training results of the convolutional neural network (CNN), and then the laser stripe is accurately detected by non-feature filtering criteria based on area constraints. Finally, a sub-regional feature clustering method is proposed to realize effective segmentation of uneven laser stripes. A large number of verification experiments have been carried out in both laboratory and field, and the results show that the proposed method can achieve automatic and accurate extraction of laser strips, which has strong adaptability to both the complex background in the field and the uneven brightness characteristic of laser stripes, satisfying the engineering requirements of large-scale parts field measurement.
关键词： detection and segmentation,stereo-vision measurement,CNN,laser stripe,large industrial part
更新于2025-09-16 10:30:52
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[Lecture Notes in Computer Science] Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis Volume 11043 (7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings) || Deep Learning-Based Detection and Segmentation for BVS Struts in IVOCT Images
摘要： Bioresorbable Vascular Sca?old (BVS) is the latest stent type for the treatment of coronary artery disease. A major challenge of BVS is that once it is malapposed during implantation, it may potentially increase the risks of late stent thrombosis. Therefore it is important to analyze struts malapposition during implantation. This paper presents an automatic method for BVS malapposition analysis in intravascular optical coherence tomography images. Struts are ?rstly detected by a detector trained through deep learning. Then, struts boundaries are segmented using dynamic programming. Based on the segmentation, apposed and malapposed struts are discriminated automatically. Experimental results show that the proposed method successfully detected 97.7% of 4029 BVS struts with 2.41% false positives. The average Dice coe?cient between the segmented struts and ground truth was 0.809. It concludes that the proposed method is accurate and e?cient for BVS struts detection and segmentation, and enables automatic malapposition analysis.
关键词： Bioresorbable vascular sca?old,Deep learning,Intravascular optical coherence tomography,Detection and segmentation
更新于2025-09-04 15:30:14