Spatio-Temporal Multi-Task Learning for Cardiac MRI Left Ventricle Quantification

EFNet: A multitask deep learning network for simultaneous quantification of left ventricle structure and function

PURPOSE

The purpose of this study is to develop an automated method using deep learning for the reliable and precise quantification of left ventricle structure and function from echocardiogram videos, eliminating the need to identify end-systolic and end-diastolic frames. This addresses the variability and potential inaccuracies associated with manual quantification, aiming to improve the diagnosis and management of cardiovascular conditions.

METHODS

A single, fully automated multitask network, the EchoFused Network (EFNet) is introduced that simultaneously addresses both left ventricle segmentation and ejection fraction estimation tasks through cross-module fusion. Our proposed approach utilizes semi-supervised learning to estimate the ejection fraction from the entire cardiac cycle, yielding more dependable estimations and obviating the need to identify specific frames. To facilitate joint optimization, the losses from task-specific modules are combined using a normalization technique, ensuring commensurability on a comparable scale.

RESULTS

The assessment of the proposed model on a publicly available dataset, EchoNet-Dynamic, shows significant performance improvement, achieving an MAE of 4.35% for ejection fraction estimation and DSC values of 0.9309 (end-diastolic) and 0.9135 (end-systolic) for left ventricle segmentation.

CONCLUSIONS

The study demonstrates the efficacy of EFNet, a multitask deep learning network, in simultaneously quantifying left ventricle structure and function through cross-module fusion on echocardiogram data.

A Motion-Aware DNN Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine MR Sequences

Accurate segmentation of the left ventricle myocardium is the key step of automatic assessment of cardiac function. However, the current methods mainly focus on the end-diastolic and the end-systolic frames in cine MR sequences and lack the attention to myocardial motion in the cardiac cycle. Additionally, due to the lack of fine segmentation tools, the simplified approach, excluding papillary muscles and trabeculae from myocardium, is applied in clinical practice. To solve these problems, we propose a motion-aware DNN model with edge focus loss and quality control in this paper. Specifically, the bidirectional ConvLSTM layer and a new motion attention layer are proposed to encode motion-aware feature maps, and an edge focus loss function is proposed to train the model to generate the fine segmentation results. Additionally, a quality control method is proposed to filter out the abnormal segmentations before subsequent analyses. Compared with state-of-the-art segmentation models on the public dataset and the in-house dataset, the proposed method has obtained high segmentation accuracy. On the 17-segment model, the proposed method has obtained the highest Pearson correlation coefficient at 14 of 17 segments, and the mean PCC of 85%. The experimental results highlight the segmentation accuracy of the proposed method as well as its availability to substitute for the manually annotated boundaries for the automatic assessment of cardiac function.

Data pyramid structure for optimizing EUS-based GISTs diagnosis in multi-center analysis with missing label

This study introduces the Data Pyramid Structure (DPS) to address data sparsity and missing labels in medical image analysis. The DPS optimizes multi-task learning and enables sustainable expansion of multi-center data analysis. Specifically, It facilitates attribute prediction and malignant tumor diagnosis tasks by implementing a segmentation and aggregation strategy on data with absent attribute labels. To leverage multi-center data, we propose the Unified Ensemble Learning Framework (UELF) and the Unified Federated Learning Framework (UFLF), which incorporate strategies for data transfer and incremental learning in scenarios with missing labels. The proposed method was evaluated on a challenging EUS patient dataset from five centers, achieving promising diagnostic performance. The average accuracy was 0.984 with an AUC of 0.927 for multi-center analysis, surpassing state-of-the-art approaches. The interpretability of the predictions further highlights the potential clinical relevance of our method.

A task-unified network with transformer and spatial-temporal convolution for left ventricular quantification

Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial-temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial-temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.

NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation

With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for clinical diagnosis and disease treatment. The extraction and segmentation of the regions of interest in cardiac images are crucial to the diagnosis of cardiovascular diseases. Due to the erratically diastolic and systolic cardiac, the boundaries of Magnetic Resonance (MR) images are quite fuzzy. Moreover, it is hard to provide complete information using a single modality due to the complex structure of the cardiac image. Furthermore, conventional CNN-based segmentation methods are weak in feature extraction. To overcome these challenges, we propose a multi-modal method for cardiac image segmentation, called NVTrans-UNet. Firstly, we employ the Neighborhood Vision Transformer (NVT) module, which takes advantage of Neighborhood Attention (NA) and inductive biases. It can better extract the local information of the cardiac image as well as reduce the computational cost. Secondly, we introduce a Multi-modal Gated Fusion (MGF) network, which can automatically adjust the contributions of different modal feature maps and make full use of multi-modal information. Thirdly, the bottleneck layer with Atrous Spatial Pyramid Pooling (ASPP) is proposed to expand the feature receptive field. Finally, the mixed loss is added to the cardiac image to focus the fuzzy boundary and realize accurate segmentation. We evaluated our model on MyoPS 2020 dataset. The Dice score of myocardial infarction (MI) was 0.642 ± 0.171, and the Dice score of myocardial infarction + edema (MI + ME) was 0.574 ± 0.110. Compared with the baseline, the MI increases by 11.2%, and the MI + ME increases by 12.5%. The results show the effectiveness of the proposed NVTrans-UNet in the segmentation of MI and ME.

Multi-task deep learning for medical image computing and analysis: A review

The renaissance of deep learning has provided promising solutions to various tasks. While conventional deep learning models are constructed for a single specific task, multi-task deep learning (MTDL) that is capable to simultaneously accomplish at least two tasks has attracted research attention. MTDL is a joint learning paradigm that harnesses the inherent correlation of multiple related tasks to achieve reciprocal benefits in improving performance, enhancing generalizability, and reducing the overall computational cost. This review focuses on the advanced applications of MTDL for medical image computing and analysis. We first summarize four popular MTDL network architectures (i.e., cascaded, parallel, interacted, and hybrid). Then, we review the representative MTDL-based networks for eight application areas, including the brain, eye, chest, cardiac, abdomen, musculoskeletal, pathology, and other human body regions. While MTDL-based medical image processing has been flourishing and demonstrating outstanding performance in many tasks, in the meanwhile, there are performance gaps in some tasks, and accordingly we perceive the open challenges and the perspective trends. For instance, in the 2018 Ischemic Stroke Lesion Segmentation challenge, the reported top dice score of 0.51 and top recall of 0.55 achieved by the cascaded MTDL model indicate further research efforts in high demand to escalate the performance of current models.

Improving myocardial pathology segmentation with U-Net++ and EfficientSeg from multi-sequence cardiac magnetic resonance images

BACKGROUND

Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clinical experience.

METHODS

In this work, we develop an automatic and accurate coarse-to-fine myocardial pathology segmentation framework based on the U-Net++ and EfficientSeg model. The U-Net++ network with deep supervision is first applied to delineate the cardiac structures from the multi-sequence cardiac magnetic resonance (CMR) images to generate a coarse segmentation map. Then the coarse segmentation map together with the three-sequence CMR data is sent to the EfficientSeg-B1 for fine segmentation, that is, further segmentation of myocardial scar and edema areas. In addition, we apply the Focal loss to replace the original cross-entropy term, for the purpose of encouraging the model to pay more attention to the pathological areas.

RESULTS

The proposed segmentation approach is tested on the public Myocardial Pathology Segmentation Challenge (MyoPS 2020) dataset. Experimental results demonstrate that our solution achieves an average Dice score of 0.7148 ± 0.2213 for scar, an average Dice score of 0.7439 ± 0.1011 for edema + scar, and the final average score of 0.7294 on the overall 20 testing sets, all of which have outperformed the first place method in the competition. Moreover, extensive ablation experiments are performed, which shows that the two-stage strategy with Focal loss greatly improves the segmentation quality of pathological areas.

CONCLUSION

Given its effectiveness and superiority, our method can further facilitate myocardial pathology segmentation in medical practice.

Domain generalization for prostate segmentation in transrectal ultrasound images: A multi-center study

Prostate biopsy and image-guided treatment procedures are often performed under the guidance of ultrasound fused with magnetic resonance images (MRI). Accurate image fusion relies on accurate segmentation of the prostate on ultrasound images. Yet, the reduced signal-to-noise ratio and artifacts (e.g., speckle and shadowing) in ultrasound images limit the performance of automated prostate segmentation techniques and generalizing these methods to new image domains is inherently difficult. In this study, we address these challenges by introducing a novel 2.5D deep neural network for prostate segmentation on ultrasound images. Our approach addresses the limitations of transfer learning and finetuning methods (i.e., drop in performance on the original training data when the model weights are updated) by combining a supervised domain adaptation technique and a knowledge distillation loss. The knowledge distillation loss allows the preservation of previously learned knowledge and reduces the performance drop after model finetuning on new datasets. Furthermore, our approach relies on an attention module that considers model feature positioning information to improve the segmentation accuracy. We trained our model on 764 subjects from one institution and finetuned our model using only ten subjects from subsequent institutions. We analyzed the performance of our method on three large datasets encompassing 2067 subjects from three different institutions. Our method achieved an average Dice Similarity Coefficient (Dice) of 94.0±0.03 and Hausdorff Distance (HD95) of 2.28 mm in an independent set of subjects from the first institution. Moreover, our model generalized well in the studies from the other two institutions (Dice: 91.0±0.03; HD95: 3.7 mm and Dice: 82.0±0.03; HD95: 7.1 mm). We introduced an approach that successfully segmented the prostate on ultrasound images in a multi-center study, suggesting its clinical potential to facilitate the accurate fusion of ultrasound and MRI images to drive biopsy and image-guided treatments.