ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis

Abstract Background Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients’ discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning. Methods In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images. Results We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images. Conclusions The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical

A Global Inhomogeneous Intensity Clustering- (GINC-) Based Active Contour Model for Image Segmentation and Bias Correction

Image segmentation is still an open problem especially when intensities of the objects of interest are overlapped due to the presence of intensity inhomogeneities. A bias correction embedded level set model is proposed in this paper where inhomogeneities are estimated by orthogonal primary functions. First, an inhomogeneous intensity clustering energy is defined based on global distribution characteristics of the image intensities, and membership functions of the clusters described by the level set function are then introduced to define the data term energy of the proposed model. Second, a regularization term and an arc length term are also included to regularize the level set function and smooth its zero-level set contour, respectively. Third, the proposed model is extended to multichannel and multiphase patterns to segment colorful images and images with multiple objects, respectively. Experimental results and comparison with relevant models demonstrate the advantages of the proposed model in terms of bias correction and segmentation accuracy on widely used synthetic and real images and the BrainWeb and the IBSR image repositories

Segmentation of Clinical Target Volume From CT Images for Cervical Cancer Using Deep Learning

Introduction: Segmentation of clinical target volume (CTV) from CT images is critical for cervical cancer brachytherapy, but this task is time-consuming, laborious, and not reproducible. In this work, we aim to propose an end-to-end model to segment CTV for cervical cancer brachytherapy accurately. Methods: In this paper, an improved M-Net model (Mnet_IM) is proposed to segment CTV of cervical cancer from CT images. An input and an output branch are both proposed to attach to the bottom layer to deal with CTV locating challenges due to its lower contrast than surrounding organs and tissues. A progressive fusion approach is then proposed to recover the prediction results layer by layer to enhance the smoothness of segmentation results. A loss function is defined on each of the multiscale outputs to form a deep supervision mechanism. Numbers of feature map channels that are directly connected to inputs are finally homogenized for each image resolution to reduce feature redundancy and computational burden. Result: Experimental results of the proposed model and some representative models on 5438 image slices from 53 cervical cancer patients demonstrate advantages of the proposed model in terms of segmentation accuracy, such as average surface distance, 95% Hausdorff distance, surface overlap, surface dice, and volumetric dice. Conclusion: A better agreement between the predicted CTV from the proposed model Mnet_IM and manually labeled ground truth is obtained compared to some representative state-of-the-art models

A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images

Summary: Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self-supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images

5-ALA, DTA-6, and Nitrogen Mitigate NaCl Stress by Promoting Photosynthesis and Carbon Metabolism in Rice Seedlings

A large number of dead seedlings can occur in saline soils, which seriously affects the large-scale cultivation of rice. This study investigated the effects of plant growth regulators (PGRs) and nitrogen application on seedling growth and salt tolerance (Oryza sativa L.), which is of great significance for agricultural production practices. A conventional rice variety, “Huang Huazhan”, was selected for this study. Non-salt stress treatments included 0% NaCl (CK treatment), CK + 0.05 g N/pot (N treatment), CK + 40 mg·L−1 5-aminolevulinic acid (5-ALA) (A treatment), and CK + 30 mg·L−1 diethylaminoethyl acetate (DTA-6) (D treatment). Salt stress treatments included 0.3% NaCl (S treatment), N + 0.3% NaCl (NS treatment), A + 0.3% NaCl (AS treatment), and D + 0.3% NaCl (DS treatment). When 3 leaves and 1 heart emerged from the soil, plants were sprayed with DTA-6 and 5-ALA, followed by the application of 0.3% NaCl (w/w) to the soil after 24 h. Seedling morphology and photosynthetic indices, as well as carbohydrate metabolism and key enzyme activities, were determined for each treatment. Our results showed that N, A, and D treatments promoted seedling growth, photosynthesis, carbohydrate levels, and the activities of key enzymes involved in carbon metabolism when compared to the CK treatment. The A treatment had the most significant effect, with increases in aboveground dry weight and net photosynthetic rates (Pn) ranging from 17.74% to 41.02% and 3.61% to 32.60%, respectively. Stomatal limiting values (Ls) significantly decreased from 19.17% to 43.02%. Salt stress significantly inhibited seedling growth. NS, AS, and DS treatments alleviated the morphological and physiological damage of salt stress on seedlings when compared to the S treatment. The AS treatment was the most effective in improving seedling morphology, promoting photosynthesis, increasing carbohydrate levels, and key enzyme activities. After AS treatment, increases in aboveground dry weight, net photosynthetic rate, soluble sugar content, total sucrose synthase, and amylase activities were 17.50% to 50.79%, 11.39% to 98.10%, 20.20% to 80.85%, 21.21% to 33.53%, and 22.17% to 34.19%, respectively, when compared to the S treatment. In summary, foliar sprays of 5-ALA, DTA-6, and additional nitrogen fertilizer enhanced rice seedling growth, increased photosynthesis, lowered Ls values, and improved seedling salt tolerance. Spraying two regulators, 5-ALA and DTA-6, quantitatively increased the effect of nitrogen fertilizer, with comparable effects on NaCl stress regulation. This study provides the basis for efficient agricultural production