A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

Thangaraj, P.; Tamilselvi, P.

doi:10.4018/jiit.2012010104

Cited by 10 publications

(7 citation statements)

References 27 publications

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“…Segmentation of kidney stones. Several methods such as Region indicator with Contour segmentation (RICS) 9 , modified watershed segmentation 10 , and squared euclidean distance method 11 have been implemented for the detection and segmentation of kidney stones in ultrasound (US) images. Some studies have also explored techniques such as intensity, location and size based thresholds 12 , Fuzzy C-means clustering followed by level set 13 , and CNN 14 for detection and segmentation of kidney stones in CT images.…”

Section: Semantic Segmentationmentioning

confidence: 99%

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

Gupta¹,

Ali²,

Goldsmith³

et al. 2021

Preprint

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Kidney stones represent a considerable burden for public health-care systems, with the total health-care expenditure for kidney stones exceeding US $ 2 billion annually in the USA alone. Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment of kidney stones. Automated segmentation of kidney stones and laser fiber is an important initial step to performing any automated quantitative analysis of the stones, particularly stone-size estimation, that can be used by the surgeon to decide if the stone requires further fragmentation. Factors such as turbid fluid inside the cavity, specularities, motion blur due to kidney movements and camera motion, bleeding, and stone debris impact the quality of vision within the kidney and lead to extended operative times. To the best of our knowledge, this is the first attempt made towards multi-class segmentation in ureteroscopy and laser lithotripsy data. We propose an end-to-end convolution neural network (CNN) based learning framework for the segmentation of stones and laser fiber. The proposed approach utilizes two sub-networks: I) HybResUNet, a hybrid version of residual U-Net, that uses residual connections in the encoder path of U-Net to improve semantic predictions, and II) a DVFNet that generates deformation vector field (DVF) predictions by leveraging motion differences between the adjacent video frames which is then used to prune the prediction maps. We also present ablation studies that combine different dilated convolutions, recurrent and residual connections, atrous spatial pyramid pooling and attention gate model. Further, we propose a compound loss function that significantly boosts the segmentation performance in our data. We have also provided an ablation study to determine the optimal data augmentation strategy for our dataset. Our qualitative and quantitative results illustrate that our proposed method outperforms state-of-the-art methods such as UNet and DeepLabv3+ showing an improvement of 5.2% and 15.93%, respectively, for the combined mean of DSC and JI in our in vivo test dataset. We also show that our proposed model generalizes better on a new clinical dataset showing a mean improvement of 25.4%, 20%, and 11% over UNet, HybResUNet, and DeepLabv3+, respectively, for the same metric.

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Section: Semantic Segmentationmentioning

confidence: 99%

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

Gupta¹,

Ali²,

Goldsmith³

et al. 2021

Preprint

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“…A variety of automatic methods have been developed for segmenting kidneys in 2D US images, including active contour model (ACM) based methods (5, 6), atlas-based methods (7), Markov random field based methods (8), watershed based methods (9), machine learning based methods (10), and deep learning methods (11, 12). Among them, the ACM based method is appealing for its robustness to imaging noise, weak boundaries, and large appearance variation within kidneys.…”

Section: Introductionmentioning

confidence: 99%

“…Automatic segmentation of ultrasound images of kidneys will facilitate extraction and quantification of features such as renal parenchymal area and kidney echogenicity, which currently are measured manually or are subjective assessments, respectively. A variety of methods have been proposed for segmenting kidneys in US images, including active contour model (ACM) based methods (Yang, Qin et al 2012, Song, Wang et al 2016, atlas-based methods (Marsousi, Plataniotis et al 2015), Markov random field based methods (Martin-Fernandez and Alberola-Lopez 2005), watershed based methods (Tamilselvi and Thangaraj 2012), and machine learning based methods (Ardon, Cuingnet et al 2015). Among them, the ACM based method is appealing for its robustness to imaging noise, weak boundaries, and large appearance variation within kidneys.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images

et al. 2018

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“…In the first phase, all bright spots are identified as potential calculi using the algorithms, automatic segmentation (Marques, 2011), active contour models (Umbaugh, 2010), Pyramidal seeded region growing algorithm (Tomori et al, 1999), Morphology based segmentation (Tamilselvi and Thangaraj, 2012), Merge and split (Marques, 2011) and region growing algorithm (Sridhar, 2011). The object contour is then encoded, smoothened and connected.…”

Section: Proposed Methodmentioning

confidence: 99%

“…This is improved by Pyramidal seeded region growing algorithm (Tomori et al, 1999) uses multiple seeds that grow simultaneously for segmenting the region of interest. Morphology based segmentation (Tamilselvi and Thangaraj 2012) is also useful in extracting the calculi present in the ultrasound images. An attempt is also made to use active contours or snakes to find object boundaries (Umbaugh, 2010).…”

Section: Related Workmentioning

confidence: 99%

Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

Sridhar¹

2012

Journal of Computer Science

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Problem statement:The framework for segmenting calculi in ureter and bladder using ultrasound images is proposed in this study. Calculi are due to abnormal collection of certain chemicals like oxalate, phosphate and uric acid. These calculi can be present in kidney, urethra or in urinary bladder. The extraction of calculi in medical images is a difficult task as no standard algorithms are available. This poses a serious a problem for successful diagnosis of calculi disease. The proposed technique is specific for the extraction of calculi in ureter and bladder. This constitutes the first stage in the successful treatment of calculi disease. Approach: An algorithm is proposed to detect calculus based on the shadow it casts in ultrasound image. Calculi are present in ultrasound images as bright spots. But noise in the image also can be bright spots. So it is easy to misinterpret the presence of noise as calculi. The proposed framework thus has two phases. In phase one, five standard algorithms are modified and are used to segment the bright spots present in the ultrasound images using the intensity profile. Calculi are having intensity in the range of 10-40. So all the potential calculi as well as the noise that appear as bright spots are segmented in phase one. In phase two,a validation procedure is used to validate the presence of calculi using its acoustic shadow property in the ultrasound images. Results: Ultrasound images of twenty-seven ureteric and bladder calculi patients are used for testing the framework. The detected calculi by the proposed framework are validated against a group of experts. The Performance of the proposed method is thoroughly investigated and the accuracy of the framework is determined. The framework incorporating automated seed selected region growing algorithm is able to detect the calculi with the efficiency of 78.57%. Conclusion: The extracted calculi can further be analyzed for taking decision about the treatment procedures. The proposed system is helpful for taking decision about the treatment procedures. The proposed system is helpful for clinicians as a decision support tool. This system can also be useful as educational aid for assisting or decision making in the treatment of calculus disease.

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A Modified Watershed Segmentation Method to Segment Renal Calculi in Ultrasound Kidney Images

Cited by 10 publications

References 27 publications

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy

A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images

Segmentation of Ureteric and Bladder Calculi in Ultrasound Images

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