Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation

Kattula S, Avula A, Baradhi K (2021) Anasarca, StatPearls. StatPearls Publishing, Treasure Island (FL)

Google Scholar 

Having K, Bullock S (2011) Fetal anasarca. J Diagn Med Sonogr 27(1):19–25

Article  Google Scholar 

Bobkova I, Chebotareva N, Kozlovskaya L, Shilov E (2016) Edema in renal diseases-current view on pathogenesis. Nephrol Point Care 2(1):5000204

Article  Google Scholar 

Dierckx R, Haine SE, Vrints CJ, Paelinck BP (2008) Young adult with congenital heart disease presenting with anasarca. Circulation 118(12):1304–1305

Article  PubMed  Google Scholar 

Mylona E, Golfinopoulou S, Sfakianaki P, Kyriakopoulos G, Tsonis I, Apostolou T, Vourlakou C, Skoutelis A (2016) Intravascular lymphoma as an uncommon cause of anasarca. Eur J Case Rep Int Med 3(5):000424

Google Scholar 

Liu J, Shafaat O, Summers RM (2023) A Gaussian mixture model to segment subcutaneous edema on abdominal CT. In 20th IEEE international symposium on biomedical imaging. Cartagena de Indias, Colombia

Liu J, Shafaat O, Bhadra S, Parnell C, Harris A, Summers RM (2024) Improved subcutaneous edema segmentation on abdominal CT using a generated adipose tissue density prior. Int J Comput Assist Radiol Surg 19(3):443–448

Article  PubMed  PubMed Central  Google Scholar 

Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

Article  CAS  PubMed  Google Scholar 

Malladi R, Sethian JA (1996) Level set and fast marching methods in image processing and computer vision. In Proceedings of 3rd IEEE international conference on image processing, 1, 489–492 . IEEE

Isola P, Zhu J-Y, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1125–1134

Papandreou G, Chen L-C, Murphy KP, Yuille AL (2015) Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In proceedings of the IEEE international conference on computer vision, 1742–1750

Kervadec H, Dolz J, Tang M, Granger E, Boykov Y, Ayed IB (2019) Constrained-CNN losses for weakly supervised segmentation. Med Image Anal 54:88–99

Article  PubMed  Google Scholar 

Dalca AV, Guttag J, Sabuncu MR (2018) Anatomical priors in convolutional networks for unsupervised biomedical segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, 9290–9299

Mathai TS, Liu B, Summers RM (2024) Segmentation of mediastinal lymph nodes in CT with anatomical priors. Int J Comput Assist Radiol Surg 19:1–8

Article  Google Scholar 

Bouget D, Pedersen A, Vanel J, Leira HO, Langø T (2023) Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding. Comput Methods Biomech Biomed Eng: Imag Visual 11(1):44–58

Google Scholar 

Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211

Article  CAS  PubMed  Google Scholar 

Isensee F, Wald T, Ulrich C, Baumgartner M, Roy S, Maier-Hein K, Jaeger PF (2024) nnU-Net revisited: a call for rigorous validation in 3D medical image segmentation. arXiv preprint arXiv:2404.09556

Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll DT, Cyriac J, Yang S et al (2023) Totalsegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 5(5):e230024

Article  PubMed  PubMed Central  Google Scholar 

Diaz-Pinto A, Alle S, Nath V, Tang Y, Ihsani A, Asad M, Pérez-García F, Mehta P, Li W, Flores M (2024) MONAI Label: a framework for ai-assisted interactive labeling of 3D medical images. Med Image Anal 95:103207

Liu J, Shafaat O, Summers RM (2023) A dual-branch network with mixed and self-supervision for medical image segmentation: an application to segment edematous adipose tissue. In Workshop on medical image learning with limited and noisy data, 158–167 . Springer

Hou B, Mathai TS, Liu J, Parnell C, Summers RM (2024) Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study. Int J Comput Assist Radiol Surg 19:1–8

Article  Google Scholar 

Moore CM, Van Thiel DH (2013) Cirrhotic ascites review: pathophysiology, diagnosis and management. World J Hepatol 5(5):251

Article  PubMed  PubMed Central  Google Scholar 

Hou B, Lee S-W, Lee J-M, Koh C, Xiao J, Pickhardt PJ, Summers RM (2024) Deep learning segmentation of ascites on abdominal CT scans for automatic volume quantification. Radiol Artif Intell 6:230601

Article  Google Scholar 

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, Peterson P, Weckesser W, Bright J, van der Walt SJ, Brett M, Wilson J, Millman KJ, Mayorov N, Nelson ARJ, Jones E, Kern R, Larson E, Carey CJ, Polat İ, Feng Y, Moore EW, VanderPlas J, Laxalde D, Perktold J, Cimrman R, Henriksen I, Quintero EA, Harris CR, Archibald AM, Ribeiro AH, Pedregosa F, van Mulbregt P (2020) SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nat Methods 17:261–272. https://doi.org/10.1038/s41592-019-0686-2

Article  CAS  PubMed  PubMed Central  Google Scholar 

Reinke A, Tizabi MD, Baumgartner M, Eisenmann M, Heckmann-Nötzel D, Kavur AE, Rädsch T, Sudre CH, Acion L, Antonelli M (2024) Understanding metric-related pitfalls in image analysis validation. Nat Methods 21(2):182–194

Article  CAS  PubMed  PubMed Central  Google Scholar 

Maier-Hein L, Reinke A, Godau P, Tizabi MD, Buettner F, Christodoulou E, Glocker B, Isensee F, Kleesiek J, Kozubek M (2024) Metrics reloaded: recommendations for image analysis validation. Nat Methods 21(2):195–212

Article  CAS  PubMed  PubMed Central  Google Scholar 

Mensink R, Paans W, Renes M, Dieperink W, Blokzijl F (2024) Fluid balance versus weighing: a comparison in icu patients: a single center observational study. PLoS ONE 19(4):0299474

Article  Google Scholar 

Dopierala C, Guméry P-Y, Frikha M-R, Thiebault J-J, Junot S, Defaye P, Carabelli A, Tuvignon P, Remond D, Hermet J (2020) A new gastric impedancemeter for detecting the development of a visceral edema: a proof-of-concept study on an experimental endotoxemic shock. In 2020 42nd annual international conference of the ieee engineering in medicine & biology society (EMBC), 4433–4436 . IEEE

Shimizu A, Kawai M, Hirono S, Okada K-I, Miyazawa M, Kitahata Y, Ueno M, Hayami S, Miyamoto A, Kimoto Y (2018) Postoperative visceral tissue edema assessed by computed tomography is a predictor for severe complications after pancreaticoduodenectomy. J Gastrointest Surg 22(1):77–87

Article  PubMed  Google Scholar 

Dlova NC, Jacobs T, Singh S (2022) Pericardial, pleural effusion and anasarca: a rare complication of low-dose oral minoxidil for hair loss. JAAD Case Rep 28:94–96

Article  PubMed  PubMed Central  Google Scholar 

Stull J (2021) Anasarca and malignant pleural effusion. Chest 160(4):1584

Article  Google Scholar 

留言 (0)

沒有登入
gif