Substantia nigra (SN) has been reported as significantly related to the progression of Parkinson’s Disease (PD). Fully automated segmentation of SN is an important step for developing an interpretable computer-aided diagnosis system for PD. Based on the deep learning techniques, this paper proposes a novel distance-reweighted loss function and combines it with the test-time normalization (TTN) to boost the fully automated SN segmentation accuracy from low contrast T2 weighted MRI. The proposed loss encourages the model to focus on the suspicious regions with vague boundaries, and the involved TTN narrows the gap between an input MRI volume and the reference MRI volumes in test-time. The results showed that both the proposed loss and TTN could help improve the segmentation accuracy. By combining the proposed loss and TTN, the averaged Dice coefficient achieved 70.90% from T2 weighted MRI, compared to 68.17% by the baseline method.
Neuromelanin magnetic resonance imaging (NM-MRI) has been widely used in the diagnosis of Parkinson’s disease (PD) for its significantly enhanced contrast between the PD-related structure, the substantia nigra (SN) and surrounding tissues. This paper proposes a novel network combining the priority gating attention and Bayesian learning for improving the accuracy of fully automatic SN segmentation from NM-MRI. Different from the conventional gated attention model, the proposed network uses the prior SN probability map for guiding the attention computation. Additionally, to lower the risks of over-fitting and estimate the confidence scores for the segmentation results, Bayesian learning with Monte Carlo dropout is applied in the training and testing phases. The quantitative results showed that the proposed network acquired the averaged Dice score of 79.46% in comparison with the baseline model 77.93%.
Automatic segmentation of the Parkinson’s disease-related tissue, the substantia nigra (SN), is an important step towards the accurate computer-aided diagnosis systems. Recently deep learning methods have achieved the state-of-the-art performance of the automated segmentation in various scenarios of medical image analysis. However, to acquire high resolution segmentation results, the conventional deep learning frameworks depend heavily on the full size of annotated data, which is pretty time-consuming and expensive for the training of the model. Moreover, the SN structure is usually tiny and sensitive to the progression of Parkinson’s disease (PD), which brings more anatomic variations among cases. To deal with these problems, this paper combines the cascaded fully convolutional network (FCN) and the size-reweighted loss function to automatically segment the tiny subcortical tissue SN from T2 MRI volumes. Different from the conventional one-stage FCNs, we cascade two FCNs in a coarse-to-fine fashion for the high resolution segmentation of the SN. The first FCN is trained to locate the SN-contained ROI and produce a coarse segmentation mask from a down-sampled MRI volume. The second FCN solely segments the SN at full resolution based on the results of the first FCN. Additionally, by giving higher weights to the SN region, the size-reweighted loss function encourages the model to concentrate on the tiny SN structure. Our results showed that the proposed FCN achieves mean dice score of 68.92% in comparison with the baseline model 66.40%.
Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.
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