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A modified rock mass classification system for TBM tunnels is proposed based on the hydropower rock mass classification (HC) method used in China. Rock mass properties and TBM performance data from three tunnelling projects were used for regression analysis. The proposed method considers the rock mass boreability and stability, and estimates the penetration per revolution, utilisation, and advance rate of a TBM under various rock mass conditions. The influence of adverse geological conditions (rockbursts, squeezing, faults, highly abrasive rock, and water inrush) on TBM performance is also considered. The classification system can be used to evaluate the feasibility of TBM construction and serve as the basis for selection of TBM types and design parameters in the pre-construction phase. It can also be applied to estimate TBM performance and guide tunnel support for a given ground condition in the design phase, and to optimise TBM operational parameters during the construction phase. A 7.9 m diameter section of the YE water conveyance tunnel, which was driven through granite in Xinjiang, is used to illustrate the application of the modified rock mass classification system., A modified rock mass classification system for TBM tunnels is proposed based on the hydropower rock mass classification (HC) method used in China. Rock mass properties and TBM performance data from three tunnelling projects were used for regression analysis. The proposed method considers the rock mass boreability and stability, and estimates the penetration per revolution, utilisation, and advance rate of a TBM under various rock mass conditions. The influence of adverse geological conditions (rockbursts, squeezing, faults, highly abrasive rock, and water inrush) on TBM performance is also considered. The classification system can be used to evaluate the feasibility of TBM construction and serve as the basis for selection of TBM types and design parameters in the pre-construction phase. It can also be applied to estimate TBM performance and guide tunnel support for a given ground condition in the design phase, and to optimise TBM operational parameters during the construction phase. A 7.9 m diameter section of the YE water conveyance tunnel, which was driven through granite in Xinjiang, is used to illustrate the application of the modified rock mass classification system.

Hui B, Xu Y, Zhao B, et al. Onco Targets Ther. 2019;12:1043–1057. The Editor and Publisher of OncoTargets and Therapy wish to retract the published article. Concerns were raised over alleged image duplication between Figures 2F and 6E. Specifically: Figure 2F, panel AsPC-1 Empty vector appears to have been duplicated with Figure 6E, panel BxPC-3 si-NC. The author responded to our queries but were unable to provide a satisfactory explanation for the alleged duplication and could not provide the original data for the study. The Editor requested for the article to be retracted and the authors agreed with this decision. The authors wish to apologise for this error. Our decision-making was informed by our policy on publishing ethics and integrity and the COPE guidelines on retraction. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”. This retraction relates to this paper

The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data. Few-shot Semantic Segmentation (FSS) is a promising strategy for breaking the deadlock. However, a high-performing FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting, which leads to its performance bottleneck in medical image segmentation due to the unmet need for annotations. Thus, semi-supervised FSS for medical images is accordingly proposed to utilize unlabeled data for further performance improvement. Nevertheless, existing semi-supervised FSS methods has two obvious defects: (1) neglecting the relationship between the labeled and unlabeled data; (2) using unlabeled data directly for end-to-end training leads to degenerated representation learning. To address these problems, we propose a novel semi-supervised FSS framework for medical image segmentation. The proposed framework employs Poisson learning for modeling data relationship and propagating supervision signals, and Spatial Consistency Calibration for encouraging the model to learn more coherent representations. In this process, unlabeled samples do not involve in end-to-end training, but provide supervisory information for query image segmentation through graph-based learning. We conduct extensive experiments on three medical image segmentation datasets (i.e. ISIC skin lesion segmentation, abdominal organs segmentation for MRI and abdominal organs segmentation for CT) to demonstrate the state-of-the-art performance and broad applicability of the proposed framework., Comment: Accepted by 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021)

Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation. However, those methods have two primary limitations: the first-stage segmentation becomes a performance bottleneck; the lack of overall differentiability makes the training process of two stages asynchronous and inconsistent. In this paper, we propose a differentiable two-stage network architecture to tackle these problems. In the first stage, a localization network (L-Net) locates Regions of Interest (RoIs) in a detection fashion; in the second stage, a segmentation network (S-Net) performs fine segmentation on the recalibrated RoIs; a RoI recalibration module between L-Net and S-Net eliminating the inconsistencies. Experimental results on the public dataset show that our method outperforms state-of-the-art coarse-to-fine models with negligible computation overheads.

Automatic segmentation of the prostate cancer from the multi-modal magnetic resonance images is of critical importance for the initial staging and prognosis of patients. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the extracted attention maps of different modalities enable the model to transfer the significant spatial information with more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI with biopsy confirmed. Extensive experiment results demonstrate that our proposed network achieves state-of-the-art performance., Comment: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Kang He,1,* Yajing Wang,1,* Yuejiao Zhong,2 Xiaohua Pan,1 Lixiang Si,1 Jianwei Lu1 1The Department of Oncology, The Affiliated Cancer Hospital of Nanjing Medical University, and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, People’s Republic of China; 2The Department of Oncology, Jiangsu Cancer Hospital, and the Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Institute of Cancer Research, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jianwei LuThe Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing, People’s Republic of ChinaEmail lujw@medmail.com.cnObjective: To investigate the connection between mutant KRAS/NRAS/BRAF and clinicopathological characteristics in therapy-naïve synchronous metastatic colorectal cancer (mCRC) in Chinese populations when compared with all wild type (KRAS/NRAS/BRAF wild type).Patients and Methods: A total of 200 patients with therapy-naïve synchronous mCRC (TNM stage: TanyNanyM1) were retrospectively collected as study objects. Primary tumor tissues from 200 mCRC patients were analyzed through next-generation sequencing panel to assess the mutated regions of KRAS/NRAS/BRAF.Results: The distribution frequency of gene mutation in our study was 41% KRAS, 4% NRAS, 11.5% BRAF, 0.5% both KRAS and BRAF. Tumors with any gene mutations (any gene mutations in KRAS/NRAS/BRAF), KRAS and KRAS codon 12 mutation were more likely to be located in right-sided colon (P=0.007, P=0.008, P=0.026, respectively). For metastasis, tumors with any gene mutations, KRAS and KRAS codon 12 mutation were significantly correlated with peritoneal metastasis (P=0.019, P=0.017, P=0.014, respectively), liver-peritoneum metastases (P=0.004, P=0.003, P=0.002, respectively) and multi-organ metastases (P=0.002, P=0.008, P=0.001, respectively). Tumor