Long-short diffeomorphism memory network for weakly-supervised ultrasound landmark tracking

Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition.  Accurate tracking of an anatomical landmark has been of high inter-est for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy.  However, tracking an anatomical landmark accurately in ul-trasound video is very challenging, due to landmark deformation, visual ambiguity andpartial observation. In this paper, we propose a long-short diffeomorphism memory net-work (LSDM), which is a multi-task framework with an auxiliary learnable deformationprior to supporting accurate landmark tracking.  Specifically, we design a novel diffeo-morphic representation, which contains both long and short temporal information storedin separate memory banks for delineating motion margins and reducing cumulative er-rors.   We further propose an expectation maximization memory alignment (EMMA)algorithm to iteratively optimize both the long and short deformation memory, updatingthe  memory  queue  for  mitigating  local  anatomical  ambiguity.   The  proposed  multi-task system can be trained in a weakly-supervised manner,  which only requires fewlandmark annotations for tracking and zero annotation for deformation learning.  Weconduct extensive experiments on both public and private ultrasound landmark track-ing datasets.  Experimental results show that LSDM can achieve better or competitivelandmark tracking performance with a strong generalization capability across differentscanner types and different ultrasound modalities, compared with other state-of-the-artmethods.</p

Long-short diffeomorphism memory network for weakly-supervised ultrasound landmark tracking

Ultrasound is a promising medical imaging modality benefiting from low-cost and real-time acquisition.  Accurate tracking of an anatomical landmark has been of high inter-est for various clinical workflows such as minimally invasive surgery and ultrasound-guided radiation therapy.  However, tracking an anatomical landmark accurately in ul-trasound video is very challenging, due to landmark deformation, visual ambiguity andpartial observation. In this paper, we propose a long-short diffeomorphism memory net-work (LSDM), which is a multi-task framework with an auxiliary learnable deformationprior to supporting accurate landmark tracking.  Specifically, we design a novel diffeo-morphic representation, which contains both long and short temporal information storedin separate memory banks for delineating motion margins and reducing cumulative er-rors.   We further propose an expectation maximization memory alignment (EMMA)algorithm to iteratively optimize both the long and short deformation memory, updatingthe  memory  queue  for  mitigating  local  anatomical  ambiguity.   The  proposed  multi-task system can be trained in a weakly-supervised manner,  which only requires fewlandmark annotations for tracking and zero annotation for deformation learning.  Weconduct extensive experiments on both public and private ultrasound landmark track-ing datasets.  Experimental results show that LSDM can achieve better or competitivelandmark tracking performance with a strong generalization capability across differentscanner types and different ultrasound modalities, compared with other state-of-the-artmethods.</p

TRPM7 in CHBP-induced renoprotection upon ischemia reperfusion-related injury.

Transient receptor potential melastatin 7 (TRPM7) is a membrane ion channel and kinase. TRPM7 was abundantly expressed in the kidney, and up-regulated by ischemia reperfusion (IR) injury. Our previous studies showed that cyclic helix B peptide (CHBP) improved renal IR-related injury, but its underlying mechanism is not well defined. IR-related injury was established in renal tubular epithelial cells (TCMK-1 and HK-2) via 12 to 24-h hypoxia (H) followed by 2-24 h reoxygenation (R), and in mouse kidneys subjected to 30-min ischemia and 12-h to 7-day reperfusion. TRPM7-like current in TCMK-1 cells, TRPM7 mRNA and protein in the in vitro and in vivo models were increased, but reversed by CHBP. TRPM7 was also positively associated with LDH, HMGB1, caspase-3, Bax/Bcl-2, inflammation, apoptosis, tubulointerstitial damage and renal function respectively. Furthermore, silencing TRPM7 improved injury parameters, renal histology and function in the both models. Specific TRPM7 agonist, bradykinin, exaggerated HR induced injury in TCMK-1 cells, and partially blocked the renoprotection of CHBP as well. In conclusion, TRPM7 is involved not only in IR-related injury, but also CHBP-induced renoprotection, which are through its ion channel and subsequent affects inflammation and apoptosis. Therefore, TRPM7 could be a potential biomarker for IR-induced acute kidney injury