Hematoma Expansion Context Guided Intracranial Hemorrhage Segmentation and Uncertainty Estimation

Multi-scale object equalization learning network for intracerebral hemorrhage region segmentation

Segmentation and the subsequent quantitative assessment of the target object in computed tomography (CT) images provide valuable information for the analysis of intracerebral hemorrhage (ICH) pathology. However, most existing methods lack a reasonable strategy to explore the discriminative semantics of multi-scale ICH regions, making it difficult to address the challenge of complex morphology in clinical data. In this paper, we propose a novel multi-scale object equalization learning network (MOEL-Net) for accurate ICH region segmentation. Specifically, we first introduce a shallow feature extraction module (SFEM) for obtaining shallow semantic representations to maintain sufficient and effective detailed location information. Then, a deep feature extraction module (DFEM) is leveraged to extract the deep semantic information of the ICH region from the combination of SFEM and original image features. To further achieve equalization learning in different scales of ICH regions, we introduce a multi-level semantic feature equalization fusion module (MSFEFM), which explores the equalized fusion features of the described objects with the assistance of shallow and deep semantic information provided by SFEM and DFEM. Driven by the above three designs, MOEL-Net shows a solid capacity to capture more discriminative features in various ICH region segmentation. To promote the research of clinical automatic ICH region segmentation, we collect two datasets, VMICH and FRICH (divided into Test A and Test B) for evaluation. Experimental results show that the proposed model achieves the Dice scores of 88.28%, 90.92%, and 90.95% on the VMICH, FRICH Test A, and Test B, respectively, which outperform fourteen competing methods.

UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiation therapy, and critical robotic surgery. Automatic organ segmentation from medical images is a challenging task due to the inconsistent shape and size of different organs. Besides this, low contrast at the edges of organs due to similar types of tissue confuses the network's ability to segment the contour of organs properly. In this paper, we propose a novel convolution neural network based uncertainty-driven boundary-refined segmentation network (UDBRNet) that segments the organs from CT images. The CT images are segmented first and produce multiple segmentation masks from multi-line segmentation decoder. Uncertain regions are identified from multiple masks and the boundaries of the organs are refined based on uncertainty data. Our method achieves remarkable performance, boasting dice accuracies of 0.80, 0.95, 0.92, and 0.94 for Esophagus, Heart, Trachea, and Aorta respectively on the SegThor dataset, and 0.71, 0.89, 0.85, 0.97, and 0.97 for Esophagus, Spinal Cord, Heart, Left-Lung, and Right-Lung respectively on the LCTSC dataset. These results demonstrate the superiority of our uncertainty-driven boundary refinement technique over state-of-the-art segmentation networks such as UNet, Attention UNet, FC-denseNet, BASNet, UNet++, R2UNet, TransUNet, and DS-TransUNet. UDBRNet presents a promising network for more precise organ segmentation, particularly in challenging, uncertain conditions. The source code of our proposed method will be available at https://github.com/riadhassan/UDBRNet.

A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods

The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning

Intracerebral hemorrhage is the subtype of stroke with the highest mortality rate, especially when it also causes secondary intraventricular hemorrhage. The optimal surgical option for intracerebral hemorrhage remains one of the most controversial areas of neurosurgery. We aim to develop a deep learning model for the automatic segmentation of intraparenchymal and intraventricular hemorrhage for clinical catheter puncture path planning. First, we develop a 3D U-Net embedded with a multi-scale boundary aware module and a consistency loss for segmenting two types of hematoma in computed tomography images. The multi-scale boundary aware module can improve the model's ability to understand the two types of hematoma boundaries. The consistency loss can reduce the probability of classifying a pixel into two categories at the same time. Since different hematoma volumes and locations have different treatments. We also measure hematoma volume, estimate centroid deviation, and compare with clinical methods. Finally, we plan the puncture path and conduct clinical validation. We collected a total of 351 cases, and the test set contained 103 cases. For intraparenchymal hematomas, the accuracy can reach 96 % when the proposed method is applied for path planning. For intraventricular hematomas, the proposed model's segmentation efficiency and centroid prediction are superior to other comparable models. Experimental results and clinical practice show that the proposed model has potential for clinical application. In addition, our proposed method has no complicated modules and improves efficiency, with generalization ability.

A symmetric prior knowledge based deep learning model for intracerebral hemorrhage lesion segmentation

Background: Accurate localization and classification of intracerebral hemorrhage (ICH) lesions are of great significance for the treatment and prognosis of patients with ICH. The purpose of this study is to develop a symmetric prior knowledge based deep learning model to segment ICH lesions in computed tomography (CT). Methods: A novel symmetric Transformer network (Sym-TransNet) is designed to segment ICH lesions in CT images. A cohort of 1,157 patients diagnosed with ICH is established to train (n = 857), validate (n = 100), and test (n = 200) the Sym-TransNet. A healthy cohort of 200 subjects is added, establishing a test set with balanced positive and negative cases (n = 400), to further evaluate the accuracy, sensitivity, and specificity of the diagnosis of ICH. The segmentation results are obtained after data pre-processing and Sym-TransNet. The DICE coefficient is used to evaluate the similarity between the segmentation results and the segmentation gold standard. Furthermore, some recent deep learning methods are reproduced to compare with Sym-TransNet, and statistical analysis is performed to prove the statistical significance of the proposed method. Ablation experiments are conducted to prove that each component in Sym-TransNet could effectively improve the DICE coefficient of ICH lesions. Results: For the segmentation of ICH lesions, the DICE coefficient of Sym-TransNet is 0.716 ± 0.031 in the test set which contains 200 CT images of ICH. The DICE coefficients of five subtypes of ICH, including intraparenchymal hemorrhage (IPH), intraventricular hemorrhage (IVH), extradural hemorrhage (EDH), subdural hemorrhage (SDH), and subarachnoid hemorrhage (SAH), are 0.784 ± 0.039, 0.680 ± 0.049, 0.359 ± 0.186, 0.534 ± 0.455, and 0.337 ± 0.044, respectively. Statistical results show that the proposed Sym-TransNet can significantly improve the DICE coefficient of ICH lesions in most cases. In addition, the accuracy, sensitivity, and specificity of Sym-TransNet in the diagnosis of ICH in 400 CT images are 91.25%, 98.50%, and 84.00%, respectively. Conclusion: Compared with recent mainstream deep learning methods, the proposed Sym-TransNet can segment and identify different types of lesions from CT images of ICH patients more effectively. Moreover, the Sym-TransNet can diagnose ICH more stably and efficiently, which has clinical application prospects.

Uncertainty-guided symmetric multi-level supervision network for 3D left atrium segmentation in late gadolinium-enhanced MRI

PURPOSE

Atrial fibrillation is a common arrhythmia and requires volumetric imaging to guide the therapy procedure. Late gadolinium-enhanced magnetic resonance imaging (LGE MRI) is an efficient non-invasive technology for imaging the diseased heart. Three-dimensional segmentation of the left atrium (LA) in LGE MRI is a fundamental step for guiding the therapy of patients with atrial fibrillation. However, the low contrast and fuzzy surface of the LA in LGE MRI make accurate and objective LA segmentation challenge. The purpose of this study is to propose an automatic and efficient LA segmentation model based on a convolutional neural network to obtain a more accurate predicted surface and improve the LA segmentation results.

METHODS

In this study, we proposed an uncertainty-guided symmetric multi-level supervision network for 3D LA segmentation in LGE MRI. Firstly, we constructed a symmetric multi-level supervision structure to combine the corresponding features from the encoding and decoding stages to learn the multi-scale representation of LA. Secondly, we formulated the discrepancy of predictions of our model as model uncertainty. Then we proposed an uncertainty-guided objective function to further increase the segmentation accuracy on the surface.

RESULTS

We evaluated our proposed model on the public LA segmentation database using four universal metrics. The proposed model achieved Hausdorff Distance of 11.68 mm, average symmetric surface distance of 0.92 mm, Dice score of 0.92, and Jaccard of 0.85. Compared with state-of-the-art models, our model achieved the best Hausdorff Distance that is sensitive to surface accuracy. For the other three metrics, our model also achieved better or comparable performance.

CONCLUSIONS

We proposed an efficient automatic LA segmentation model that consisted of a symmetric multi-level supervision structure and an uncertainty-guided objective function. Compared to other models, we designed an additional supervision branch in the encoding stage to learn more detailed representations of LA while learning global context information through the multi-level structure of each supervision branch. To address the fuzzy surface challenge of LA segmentation in LGE MRI, we leveraged the model uncertainty to enhance the distinguishing ability of the model on the surface, thereby the predicted accuracy of the LA surface can be further increased. We conducted extensive ablation and comparative experiments with state-of-the-art models. The experiment results demonstrated that our proposed model could handle the complex structure of LA and had superior advantages in improving the segmentation performance on the surface. This article is protected by copyright. All rights reserved.