A Watershed Method for MR Renography Segmentation

Yu, Chunyan; Liu, Ying

doi:10.1109/icbeb.2012.45

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…To highlight our method's soundness, we compare it with other commonly used unsupervised methods in the literature and its 2D implementation [12]. In particular, our results were evaluated against the kidney graft segmentations obtained by Otsu thresholding [15] and watershed 3D [6] methods that are commonly used for unsupervised segmentation in a variety of studies. The obtained results are summarised in the mean and standard deviation (std) of each score on the whole test set.…”

Section: Resultsmentioning

confidence: 99%

“…The obtained results are summarised in the mean and standard deviation (std) of each score on the whole test set. Even if additional postprocessing methods are presented in the literature [16,6] in our study, we did not perform any additional postprocessing in any of the exploit methods. The lowest performance in terms of all the metrics is reported by the Otsu thresholding, which reaches a mean precision of 57.07 ± 3.03%, highlighting the high number of false positives.…”

Section: Resultsmentioning

confidence: 99%

“…Nowadays, unsupervised approaches investigate methods that are able to generate models without the need for annotations. In particular, concerning unsupervised kidney segmentation, image-based methods have been proposed to obtain a segmentation through an image processing pipeline using tools such as thresholding [4] or morphological operators [5,6]. Some other methods propose clustering frameworks based on K-means [7] or on wavelet-based clustering [8].…”

Section: Introductionmentioning

confidence: 99%

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3d Unsupervised Kidney Graft Segmentation Based On Deep Learning And Multi-Sequence Mri

Milecki

Bodard

Corréas

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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Image segmentation is one of the most popular problems in medical image analysis. Recently, with the success of deep neural networks, these powerful methods provide state of the art performance on various segmentation tasks. However, one of the main challenges relies on the high number of annotations that they need to be trained, which is crucial in medical applications. In this paper, we propose an unsupervised method based on deep learning for the segmentation of kidney grafts. Our method is composed of two different stages, the detection of the area of interest and the segmentation model that is able, through an iterative process, to provide accurate kidney draft segmentation without the need for annotations. The proposed framework works in the 3D space to explore all the available information and extract meaningful representations from Dynamic Contrast-Enhanced and T2 MRI sequences. Our method reports a dice of 89.8 ± 3.1%, Hausdorff distance at percentile 95% of 5.8±0.41mm and percentage of kidney volume difference of 5.9 ± 5.7% on a test dataset of 29 patients subject to a kidney transplant.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

3d Unsupervised Kidney Graft Segmentation Based On Deep Learning And Multi-Sequence Mri

Milecki

Bodard

Corréas

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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“…Chun-yan Yu ,Ying Li worked on a new method to avoid the over segmentation problem of this method by using total variation model to enhance the contrast and thereby attain smoothing. This nonlinear filter solved the over segmentation problem by exposing kidney regions more precisely [5] Luying Gui and Xiaoping Yang worked on a segmentation technique to extract lesions from ultra sound images. A detection framework to identify the difference between normal kidney and kidneys with lesions is designed.…”

Section: Different Methods Of Segmentation Of Kidney Region From mentioning

confidence: 99%

Segmentation of Ultrasound Abdominal Images to extract Region of Interest

Ranjitha¹

2019

IJRTE

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Abstract: Ultrasound imaging is one of the safest techniques for disease diagnosis which can be used in any part of the body. One of the major reason for using ultrasound images is the cost when compared with MRI, PET etc. Further, it is free from any radiation exposure and is an efficient technique for initial diagnosis. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney and hence conducting study on this segmented image becomes easy with an efficient segmentation technique. Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated in this paper. One of the major drawback of ultrasound image is that due to the complicated internal organs of the abdominal region, extraction of only kidney region is very challenging. This paper proposes a new technique where the collected abdominal ultrasound image is cleaned, to remove unwanted noise produced due to various interferences. After applying the filtering technique, kidney region is segmented. This extracted kidney image is subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study. The method is experimented with a reasonable number of dataset and applied the statistical performance test to check for the accuracy.

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