Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation

Ojeda-Pat, Allan; Martín-González, Anabel; Soberanis-Mukul, Roger D.

doi:10.1007/978-3-030-43364-2_11

Cited by 14 publications

(13 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Model/Methods used Cell nuclei [197] Base U-net [59], [198], [199] Residual U-net [52] Recurrent net; Residual block [53] Cascaded U-net; Residual block [73] U-net++ Cell contour [200], [201] Base U-net [40] Attention U-net Human embryo [202] Base U-net [49] Inception block Corneal nerve [105] Base U-net [36] Attention gate Chromosomes [203], [204] Base U-net Blood vessels [205] Base U-net Parasite detection in Chagas disease [206] Base U-net Sclerosis [207] Base U-net Colon gland [208] Base U-net…”

Section: Referencementioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Referencementioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Now, U-net is not only used for the segmentation of lymphoma histopathology images, but also for corneal neuropathology images [192]. In [193], the parasites in the blood are segmented by U-net to complete the experiment more efficiently.…”

Section: Analysis Of Image Segmentation In Lhiamentioning

confidence: 99%

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Li¹,

Chen²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

In the past ten years, the computing power of machine vision (MV) has been continuously improved, and image analysis algorithms have developed rapidly. At the same time, histopathological slices can be stored as digital images. Therefore, MV algorithms can provide doctors with diagnostic references. In particular, the continuous improvement of deep learning algorithms has further improved the accuracy of MV in disease detection and diagnosis. This paper reviews the applications of image processing technology based on MV in lymphoma histopathological images in recent years, including segmentation, classification and detection. Finally, the current methods are analyzed, some more potential methods are proposed, and further prospects are made.

show abstract

“…Since we are dealing with microscope images, it is important to mitigate imbalance problems, with these problems resulting from the region of interest (ROI) containing the parasites being significantly smaller in area compared to the background, and where the latter is of less interest to the use case at hand. To resolve these problems, image cropping can be used as a pre-processing method [2,7,19].…”

Section: Related Workmentioning

confidence: 99%

“…Thanks to their high effectiveness in the field of image understanding, convolutional neural networks (CNNs) have been used for both classification and segmentation purposes. A U-Net model [10] was employed for the segmentation of T. cruzi and Leishmania parasites in [2,7]. In both papers, the high imbalance, as mentioned above, was the limiting factor in feeding the raw data as input to the used network, given that it can drive the network to simply predict the most common class in the training set [2].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning

Jung

Anzaku

Ozbulak

et al. 2021

Intelligent Human Computer Interaction

View full text Add to dashboard Cite

Trypanosomosis, which is caused by the Trypanosoma parasite, is an infectious disease that affects both humans and animals. Today, the microscopic examination of a Giemsa or Wright stained blood smear from an infected individual is the standard procedure for diagnosis because of the straightforward nature of sample preparation. Unfortunately, this method is labor-intensive and prone to error, particularly resulting in false-negative scoring when parasite levels are low during chronic infections. Automating the detection of parasites in blood smear images can overcome sensitivity limitations related to a microscopic examination. We therefore propose a deep learning approach that aims at automatically classifying microscope images in terms of parasite presence or absence. To that end, we applied a ResNet18 model using a pre-processed dataset derived from microscope videos of unstained thick blood smears, with the blood smears originating from a mouse infected with Trypanosoma brucei. Our pre-processing strategy mainly involved image cropping and the application of a thresholding algorithm for facilitating effective model training. Moreover, our thresholding approach made it possible to observe a positive correlation between the percentage of parasite-related pixels in an image and the classification effectiveness.

show abstract

Convolutional Neural Network U-Net for Trypanosoma cruzi Segmentation

Cited by 14 publications

References 11 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

What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review

Automatic Detection of Trypanosomosis in Thick Blood Smears Using Image Pre-processing and Deep Learning

Contact Info

Product

Resources

About