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Updated: Oct 19, 2025
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning
Bin Sui1, Jun Lv1, Xiangrong Tong1
1School of Computer and Control Engineering, Yantai University, Yantai, China.
Purpose:
Magnetic resonance imaging (MRI) serves as an important medical imaging modality for a variety of clinical applications. However, the problem of long imaging time limited its wide usage. In addition, prolonged scan time will cause discomfort to the patient, leading to severe image artifacts. On the other hand, manually lesion segmentation is time consuming. Algorithm-based automatic lesion segmentation is still challenging, especially for accelerated imaging with low quality.
Methods:
In this paper, we proposed a multitask learning-based method to perform image reconstruction and lesion segmentation simultaneously, called "RecSeg". Our hypothesis is that both tasks can benefit from the usage of the proposed combined model. In the experiment, we validated the proposed multitask model on MR k-space data with different acceleration factors (2×, 4×, and 6×). Two connected U-nets were used for the tasks of liver and renal image reconstruction and segmentation. A total of 50 healthy subjects and 100 patients with hepatocellular carcinoma were included for training and testing. For the segmentation part, we use healthy subjects to verify organ segmentation, and hepatocellular carcinoma patients to verify lesion segmentation. The organs and lesions were manually contoured by an experienced radiologist.
Results:
Experimental results show that the proposed RecSeg yielded the highest PSNR (RecSeg: 32.39 ± 1.64 vs. KSVD: 29.53 ± 2.74 and single U-net: 31.18 ± 1.68, respectively, p < 0.05) and highest structural similarity index measure (SSIM) (RecSeg: 0.93 ± 0.01 vs. KSVD: 0.88 ± 0.02 and single U-net: 0.90 ± 0.01, respectively, p < 0.05) under 6× acceleration. Moreover, in the task of lesion segmentation, it is proposed that RecSeg produced the highest Dice score (RecSeg: 0.86 ± 0.01 vs. KSVD: 0.82 ± 0.01 and single U-net: 0.84 ± 0.01, respectively, p < 0.05).
Conclusions:
This study focused on the simultaneous reconstruction of medical images and the segmentation of organs and lesions. It is observed that the multitask learning-based method can improve performances of both image reconstruction and lesion segmentation.
Insights
This study introduces RecSeg, a multitask learning method for simultaneous MRI reconstruction and lesion segmentation. RecSeg improves image quality and segmentation accuracy, especially in accelerated MRI scans.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Magnetic Resonance Imaging (MRI) is crucial but limited by long scan times, causing patient discomfort and image artifacts.
- Manual lesion segmentation is time-consuming, and automated methods struggle with low-quality, accelerated imaging.
Purpose of the Study:
- To develop a multitask learning method (RecSeg) for simultaneous MRI reconstruction and lesion segmentation.
- To evaluate if combined reconstruction and segmentation tasks benefit from a unified model.
Main Methods:
- Proposed a novel multitask learning framework, RecSeg, integrating image reconstruction and lesion segmentation.
- Utilized two connected U-Nets for liver and renal image reconstruction and segmentation.
- Validated the model on MR k-space data with acceleration factors of 2×, 4×, and 6×, using data from 50 healthy subjects and 100 hepatocellular carcinoma patients.
Main Results:
- RecSeg achieved superior image reconstruction quality, evidenced by higher PSNR and SSIM compared to KSVD and single U-net at 6× acceleration.
- Demonstrated improved lesion segmentation accuracy with the highest Dice score for RecSeg.
- Results indicate significant performance gains (p < 0.05) in both reconstruction and segmentation tasks.
Conclusions:
- The multitask learning approach effectively addresses challenges in accelerated MRI.
- Simultaneous image reconstruction and lesion segmentation using RecSeg enhances overall performance.
- This method holds promise for faster and more accurate MRI analysis in clinical settings.
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