V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion Data

Pinheiro, Gustavo Retuci; Voltoline, Raphael; Bento, Mariana; Rittner, Letícia

doi:10.1007/978-3-030-11723-8_30

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…Brain tumor [106], [107], [110]- [112], [114]- [118] Base U-net [18], [109], [120] 3D U-net [81] Adversarial net; GAN [59], [108] Residual block [113] Dense block [87] Cascaded U-net [92] Residual block; Parallel U-net [44] Inception block; Up skip connections [45] Dense block; Inception block [119] 3D U-net; Residual block [19] 3D U-net, Inception block, Residual block Brain tissue [103], [121]- [124] Base U-net [28], [160] 3D U-net [161] 2.5D U-net [54] Residual block [101] Parallel U-net [41] Attention gate; Residual block White matter tracts [126], [127] U-net with modified skip connections [125] Base U-net [89] Cascaded U-net Fetal brain [128]- [130] Base U-net [131] Base U-net; 3D U-net Stroke lesion/thrombus [133]- [136] Base U-net [132] 3D U-net [69] Dense block; Inception block Cardiovascular structures [138], [140]- [142], [144], …”

Section: Reference Model/methods Usedmentioning

confidence: 99%

“…Various U-net models have been applied on MR images for brain tumor diagnosis [18], [19], [59], [45], [81], [106], [107], [87], [108]- [111], [92], [112]- [119], [44], [120]. U-net has also been applied on brain tissue for investigation of neurological conditions [54], [101], [103], [121]- [124], analysis of white matter tissue [125]- [127], [89], fetal brain development [128]- [131], and stroke lesions [69], [132]- [136].…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Reference Model/methods Usedmentioning

confidence: 99%

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…2. It justifies selection of V-Net architecture trained with D+CE, which perform best for segmenting MC in terms of all chosen metrics and achieves an average Dice score of 91.4 % and Cross-Entropy of 0.154, which is of the state-of-the-art level in various well-annotated segmentation reports [36,37]. For TB segmentation, V-Net also outperform 3D U-Net and 3D U-Net with attention in terms of HD and it is not much inferior in other metrics.…”

Section: Resultsmentioning

confidence: 84%

Deep Negative Volume Segmentation

Belikova,

Rogov,

Rybakov

et al. 2020

Preprint

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Clinical examination of three-dimensional image data of compound anatomical objects, such as complex joints, remains a tedious process, demanding the time and the expertise of physicians. For instance, automation of the segmentation task of the TMJ (temporomandibular joint) has been hindered by its compound three-dimensional shape, multiple overlaid textures, an abundance of surrounding irregularities in the skull, and a virtually omnidirectional range of the jaw's motion -all of which extend the manual annotation process to more than an hour per patient. To address the challenge, we invent a new angle to the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object -the so-called negative volume segmentation. Our approach is an end-to-end pipeline that comprises a V-Net for bone segmentation, a 3D volume construction by inflation of the reconstructed bone head in all directions along the normal vector to its mesh faces. Eventually confined within the skull bones, the inflated surface occupies the entire "negative" space in the joint, effectively providing a geometrical/topological metric of the joint's health. We validate the idea on the CT scans in a 50-patient dataset, annotated by experts in maxillofacial medicine, quantitatively compare the asymmetry given the left and the right negative volumes, and automate the entire framework for clinical adoption.

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“…In comparison to U-net, V-net learns a residual function at each stage and examines 3D pictures, using volumetric filters. Both networks performed outstandingly, being named a state-of-the-art in the medical image segmentation [ 55 ].…”

Section: How Do Machines Learnmentioning

confidence: 99%

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

Koteluk

Wartecki

Mazurek

et al. 2021

JPM

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With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

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V-Net and U-Net for Ischemic Stroke Lesion Segmentation in a Small Dataset of Perfusion Data

Cited by 15 publications

References 7 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Deep Negative Volume Segmentation

How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

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