Simultaneous image reconstruction and lesion segmentation in accelerated MRI using multitasking learning

Abstract 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.