Gleason, S.S.,
Sari-Sarraf, H.,
Abidi, M.A.,
Karakashian, O.,
Morandi, F.,
A new deformable model for analysis of X-ray CT images in preclinical
studies of mice for polycystic kidney disease,
MedImg(21), No. 10, October 2002, pp. 1302-1309.
IEEE Top Reference.
0301
BibRef
Selfridge, P.G.,
Prewitt, J.M.S.,
Organ Detection in Abdominal Computerized Tomography Scans:
Application to the Kidney,
CGIP(15), No. 3, March 1981, pp. 265-278.
Elsevier DOI
BibRef
8103
Oshiro, O.[Osamu],
Kamada, K.[Kumi],
Imura, M.[Masataka],
Chihara, K.[Kunihiro],
Toyota, E.[Eiji],
Ogasawara, Y.[Yasuo],
Kajiya, F.[Fumihiko],
Kidney Glomerulus Observation in Interactive VR Space,
IJIG(3), No. 4, October 2003, pp. 629-637.
0310
BibRef
Xie, J.[Jun],
Jiang, Y.F.[Yi-Feng],
Tsui, H.T.[Hung-Tat],
Segmentation of kidney from ultrasound images based on texture and
shape priors,
MedImg(24), No. 1, January 2005, pp. 45-57.
IEEE Abstract.
0501
BibRef
And:
Erratum:
MedImg(24), No. 2, February 2005, pp. 277-277.
IEEE Abstract.
0501
BibRef
Linguraru, M.G.[Marius George],
Yao, J.H.[Jian-Hua],
Gautam, R.[Rabindra],
Peterson, J.[James],
Li, Z.X.[Zhi-Xi],
Linehan, W.M.[W. Marston],
Summers, R.M.[Ronald M.],
Renal tumor quantification and classification in contrast-enhanced
abdominal CT,
PR(42), No. 6, June 2009, pp. 1149-1161.
Elsevier DOI
0902
Contrast-enhanced CT; Kidney cancer; von Hippel-Lindau syndrome;
Hereditary papillary renal carcinoma; Segmentation; Quantification;
Classification; Monitoring; Level sets; Computer-assisted radiology
BibRef
Raja, K.B.[K. Bommanna],
Madheswaran, M.,
Thyagarajah, K.,
Texture pattern analysis of kidney tissues for disorder identification
and classification using dominant Gabor wavelet,
MVA(21), No. 3, April 2010, pp. xx-yy.
Springer DOI
1003
BibRef
Gloger, O.,
Tonnies, K.D.,
Liebscher, V.,
Kugelmann, B.,
Laqua, R.,
Volzke, H.,
Prior Shape Level Set Segmentation on Multistep Generated Probability
Maps of MR Datasets for Fully Automatic Kidney Parenchyma Volumetry,
MedImg(31), No. 2, February 2012, pp. 312-325.
IEEE DOI
1202
BibRef
Khalifa, F.,
Beache, G.M.,
El-Ghar, M.A.[Mohamed A.],
El-Diasty, T.,
Gimel'farb, G.L.[Georgy L.],
Kong, M.Y.[Mai-Ying],
El-Baz, A.[Ayman],
Dynamic Contrast-Enhanced MRI-Based Early Detection of Acute Renal
Transplant Rejection,
MedImg(32), No. 10, 2013, pp. 1910-1927.
IEEE DOI
1311
Laplace equations
BibRef
El-Baz, A.[Ayman],
Gimel'farb, G.L.[Georgy L.],
El-Ghar, M.A.[Mohamed A.],
Image analysis approach for identification of renal transplant
rejection,
ICPR08(1-4).
IEEE DOI
0812
BibRef
And:
A novel image analysis approach for accurate identification of acute
renal rejection,
ICIP08(1812-1815).
IEEE DOI
0810
BibRef
Huang, J.[Jie],
Yang, X.P.[Xiao-Ping],
Chen, Y.M.[Yun-Mei],
Tang, L.M.[Li-Ming],
Ultrasound kidney segmentation with a global prior shape,
JVCIR(24), No. 7, 2013, pp. 937-943.
Elsevier DOI
1309
Active contour
BibRef
Rudra, A.K.[Ashish K.],
Chowdhury, A.S.[Ananda S.],
Elnakib, A.[Ahmed],
Khalifa, F.[Fahmi],
Soliman, A.[Ahmed],
Beache, G.[Garth],
El-Baz, A.[Ayman],
Kidney segmentation using graph cuts and pixel connectivity,
PRL(34), No. 13, 2013, pp. 1470-1475.
Elsevier DOI
1308
Kidney segmentation
BibRef
Skounakis, E.,
Banitsas, K.,
Badii, A.,
Tzoulakis, S.,
Maravelakis, E.,
Konstantaras, A.,
ATD: A Multiplatform for Semiautomatic 3-D Detection of Kidneys and
Their Pathology in Real Time,
HMS(44), No. 1, February 2014, pp. 146-153.
IEEE DOI
1403
biomedical MRI
BibRef
Hodneland, E.,
Hanson, E.A.,
Lundervold, A.,
Modersitzki, J.,
Eikefjord, E.,
Munthe-Kaas, A.Z.,
Segmentation-Driven Image Registration-Application to 4D DCE-MRI
Recordings of the Moving Kidneys,
IP(23), No. 5, May 2014, pp. 2392-2404.
IEEE DOI
1405
Educational institutions
BibRef
Gupta, A.[Abhinav],
Karmeshu,
Efficacy of Pearson distributions for characterization of gray levels
in clinical ultrasound kidney images,
SIViP(9), No. 6, September 2015, pp. 1317-1334.
WWW Link.
1509
BibRef
Cerrolaza, J.J.,
Safdar, N.,
Biggs, E.,
Jago, J.,
Peters, C.A.,
Linguraru, M.G.,
Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and
Patient-Specific Alpha Shapes,
MedImg(35), No. 11, November 2016, pp. 2393-2402.
IEEE DOI
1609
biological tissues
BibRef
Selvathi, D.,
Bama, S.,
Phase based distance regularized level set for the segmentation of
ultrasound kidney images,
PRL(86), No. 1, 2017, pp. 9-17.
Elsevier DOI
1702
Local feature
BibRef
Filippi, M.,
Desvignes, M.,
Moisan, E.,
Robust Unmixing of Dynamic Sequences Using Regions of Interest,
MedImg(37), No. 1, January 2018, pp. 306-315.
IEEE DOI
1801
blind source separation, feature extraction, image sequences,
medical image processing, radioisotope imaging,
kidney
BibRef
Marsh, J.N.,
Matlock, M.K.,
Kudose, S.,
Liu, T.,
Stappenbeck, T.S.,
Gaut, J.P.,
Swamidass, S.J.,
Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen
Sections,
MedImg(37), No. 12, December 2018, pp. 2718-2728.
IEEE DOI
1812
Kidney, Image segmentation, Training, Biological system modeling,
Rats, Immune system, Kidney, glomerulosclerosis, digital pathology,
donor organ evaluation
BibRef
Yu, Q.,
Shi, Y.,
Sun, J.,
Gao, Y.,
Zhu, J.,
Dai, Y.,
Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor
Segmentation in CT Images,
IP(28), No. 8, August 2019, pp. 4060-4074.
IEEE DOI
1907
computerised tomography, image classification,
image segmentation, image texture, kidney,
CT images
BibRef
Gadermayr, M.,
Gupta, L.,
Appel, V.,
Boor, P.,
Klinkhammer, B.M.,
Merhof, D.,
Generative Adversarial Networks for Facilitating Stain-Independent
Supervised and Unsupervised Segmentation: A Study on Kidney Histology,
MedImg(38), No. 10, October 2019, pp. 2293-2302.
IEEE DOI
1910
Image segmentation, Training, Adaptation models, Training data,
Task analysis, Data models, Pipelines, Histology,
image-to-image translation
BibRef
Alex, D.M.[Deepthy Mary],
Hepzibah Christinal, A.,
Chandy, D.A.[D. Abraham],
Singh, A.[Arvinder],
Pushkaran, M.,
Speckle noise suppression in 2D ultrasound kidney images using local
pattern based topological derivative,
PRL(131), 2020, pp. 49-55.
Elsevier DOI
2004
Speckle noise, Neighbourhood, Ultrasound, Local pattern,
Discrete Topological Derivative, Gradient, Holes
BibRef
Ferstl, S.,
Busse, M.,
Müller, M.,
Kimm, M.A.,
Drecoll, E.,
Bürkner, T.,
Allner, S.,
Dierolf, M.,
Pfeiffer, D.,
Rummeny, E.J.,
Weichert, W.,
Pfeiffer, F.,
Revealing the Microscopic Structure of Human Renal Cell Carcinoma in
Three Dimensions,
MedImg(39), No. 5, May 2020, pp. 1494-1500.
IEEE DOI
2005
Computed tomography, X-ray imaging,
Microscopy, Pathology, Tumors, Standards, Attenuation contrast,
X-ray imaging
BibRef
Li, Y.,
Polyak, D.,
Johnson, E.,
Yecies, D.,
Shevidi, S.,
de la Zerda, A.,
Gephart, M.H.,
Chu, S.,
Difference-Frequency Ultrasound Imaging With Non-Linear Contrast,
MedImg(39), No. 5, May 2020, pp. 1759-1766.
IEEE DOI
2005
Imaging, Ultrasonic imaging, Tumors, Transducers, Harmonic analysis,
Mice, Kidney, Ultrasound, animal models and imaging, image acquisition
BibRef
Arulanthu, P.[Pramila],
Perumal, E.[Eswaran],
An intelligent IoT with cloud centric medical decision support system
for chronic kidney disease prediction,
IJIST(30), No. 3, 2020, pp. 815-827.
DOI Link
2008
Adam, cloud, healthcare, IoT, logistic regression
BibRef
Rubini, L.J.[L. Jerlin],
Perumal, E.[Eswaran],
Efficient classification of chronic kidney disease by using
multi-kernel support vector machine and fruit fly optimization
algorithm,
IJIST(30), No. 3, 2020, pp. 660-673.
DOI Link
2008
CKD, fruit fly optimization, healthcare,
multi-kernel support vector machine, optimal FS
BibRef
Hussain, M.A.[Mohammad Arafat],
Hamarneh, G.[Ghassan],
Garbi, R.[Rafeef],
Cascaded Regression Neural Nets for Kidney Localization and
Segmentation-free Volume Estimation,
MedImg(40), No. 6, June 2021, pp. 1555-1567.
IEEE DOI
2106
Kidney, Location awareness, Image segmentation, Volume measurement,
Computed tomography,
Sørensen-Dice
BibRef
Narasimhulu, C.V.[C Venkata],
An automatic feature selection and classification framework for
analyzing ultrasound kidney images using dragonfly algorithm and
random forest classifier,
IET-IPR(15), No. 9, 2021, pp. 2080-2096.
DOI Link
2106
BibRef
Deng, R.N.[Rui-Ning],
Yang, H.C.[Hai-Chun],
Jha, A.[Aadarsh],
Lu, Y.Z.[Yu-Zhe],
Chu, P.[Peng],
Fogo, A.B.[Agnes B.],
Huo, Y.K.[Yuan-Kai],
Map3D: Registration-Based Multi-Object Tracking on 3D Serial Whole
Slide Images,
MedImg(40), No. 7, July 2021, pp. 1924-1933.
IEEE DOI
2107
Pathology, Estimation, Deep learning,
Strain, Object detection, Imaging, Pathology, renal pathology, MOT,
tracking
BibRef
Nan, Y.[Yang],
Li, F.Y.[Feng-Yi],
Tang, P.[Peng],
Zhang, G.[Guyue],
Zeng, C.H.[Cai-Hong],
Xie, G.T.[Guo-Tong],
Liu, Z.H.[Zhi-Hong],
Yang, G.[Guang],
Automatic fine-grained glomerular lesion recognition in kidney
pathology,
PR(127), 2022, pp. 108648.
Elsevier DOI
2205
Deep convolutional neural network, Glomerulus segmentation,
Fine-grained lesion classification, Uncertainty assessment, Kidney pathology
BibRef
Priyanka,
Kumar, D.[Dharmender],
Kidney image classification using transfer learning with convolutional
neural network,
IJCVR(12), No. 5, 2022, pp. 595-613.
DOI Link
2211
BibRef
Busse, M.,
Ferstl, S.,
Kimm, M.A.,
Hehn, L.,
Steiger, K.,
Allner, S.,
Müller, M.,
Drecoll, E.,
Bürkner, T.,
Dierolf, M.,
Gleich, B.,
Weichert, W.,
Pfeiffer, F.,
Multi-Scale Investigation of Human Renal Tissue in Three Dimensions,
MedImg(41), No. 12, December 2022, pp. 3489-3497.
IEEE DOI
2212
X-ray imaging, Kidney, Imaging, Histopathology, Computed tomography,
Image resolution, Microscopic tissue structure, X-ray staining
BibRef
Bugday, M.S.[Muhammet Serdar],
Akcicek, M.[Mehmet],
Bingol, H.[Harun],
Yildirim, M.[Muhammed],
Automatic diagnosis of ureteral stone and degree of hydronephrosis
with proposed convolutional neural network, RelieF, and
gradient-weighted class activation mapping based deep hybrid model,
IJIST(33), No. 2, 2023, pp. 760-769.
DOI Link
2303
CNN, grad-CAM method, hydroureteronephrosis, RelieF, SVM
BibRef
Fathima, M.D.[M. Dhilsath],
Hariharan, R.,
Raja, S.P.,
Multiple Imputation by Chained Equations: K-Nearest Neighbors and Deep
Neural Network Architecture for Kidney Disease Prediction,
IJIG(23), No. 2 2023, pp. 2350014.
DOI Link
2303
BibRef
Sheng, Q.[Quan],
Zhang, Y.[Yutao],
Shi, H.F.[Hai-Feng],
Jiao, Z.Q.[Zhu-Qing],
Global iterative optimization framework for predicting cognitive
function statuses of patients with end-stage renal disease,
IJIST(33), No. 3, 2023, pp. 837-852.
DOI Link
2305
cognitive function status, end-stage renal disease,
functional magnetic resonance imaging, prediction
BibRef
Cao, G.Y.[Gao-Yu],
Sun, Z.Q.[Zhan-Quan],
Wang, C.[Chaoli],
Geng, H.Q.[Hong-Quan],
Fu, H.L.[Hong-Liang],
Yin, Z.[Zhong],
Pan, M.[Minlan],
RASNet: Renal automatic segmentation using an improved U-Net with
multi-scale perception and attention unit,
PR(150), 2024, pp. 110336.
Elsevier DOI
2403
Renal automatic segmentation, Multi-scale spatial perception,
Attention mechanism, Image segmentation, Deep learning
BibRef
Wu, H.[Huisi],
Zhang, B.[Baiming],
Li, Z.[Zhuoying],
Qin, J.[Jing],
Lee, T.Y.[Tong-Yee],
3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance
for Kidney and Tumor Segmentation From CT Volumes,
Cyber(54), No. 5, May 2024, pp. 3299-3312.
IEEE DOI
2405
Tumors, Kidney, Feature extraction, Image segmentation, Shape,
Task analysis, Computed tomography,
scale-aware feature extraction (SAFE)
BibRef
Jiang, M.P.[Min-Peng],
Li, L.L.[Lei-Lei],
Xu, C.[Chao],
Li, Z.P.[Zheng-Ping],
Nie, C.[Chao],
Zheng, T.Y.[Tian-Yu],
Li, L.Y.[Long-Yu],
MDSK-Net: Multi-scale dynamic segmentation kernel network for renal
tumour endoscopic image segmentation,
IET-IPR(18), No. 11, 2024, pp. 2855-2868.
DOI Link
2409
image processing, image segmentation, medical image processing
BibRef
Han, F.[Fuchang],
Liao, S.[Shenghui],
Yuan, S.[Siming],
Wu, R.Z.[Ren-Zhong],
Zhao, Y.Q.[Yu-Qian],
Xie, Y.[Yu],
Explainable Prediction of Renal Cell Carcinoma from Contrast-Enhanced
CT Images Using Deep Convolutional Transfer Learning and the Shapley
Additive Explanations Approach,
ICIP21(3802-3806)
IEEE DOI
2201
Analytical models, Additives, Hospitals, Computed tomography,
Malignant tumors, Transfer learning, Predictive models,
SHapley Additive exPlanations
BibRef
Pollastri, F.[Federico],
Maroñas, J.[Juan],
Bolelli, F.[Federico],
Ligabue, G.[Giulia],
Paredes, R.[Roberto],
Magistroni, R.[Riccardo],
Grana, C.[Costantino],
Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image
Classification,
ICPR21(1298-1305)
IEEE DOI
2105
Temperature distribution, Biological system modeling, Biopsy,
Neural networks, Reliability engineering, Probabilistic logic,
BibRef
Casella, A.[Alessandro],
Moccia, S.[Sara],
Carlini, C.[Chiara],
Frontoni, E.[Emanuele],
de Momi, E.[Elena],
Mattos, L.S.[Leonardo S.],
NephCNN: A deep-learning framework for vessel segmentation in
nephrectomy laparoscopic videos,
ICPR21(6144-6149)
IEEE DOI
2105
Laparoscopes, Training, Shape, Surgery,
Tools, Convolutional neural networks, kidney segmentation,
blood vessel segmentation
BibRef
Chi, Y.L.[Yan-Ling],
Xu, Y.Y.[Yu-Yu],
Feng, G.[Gang],
Mao, J.W.[Jia-Wei],
Wu, S.[Sihua],
Xu, G.[Guibin],
Huang, W.M.[Wei-Min],
Segmenting Kidney on Multiple Phase CT Images using ULBNet,
ICPR21(8554-8561)
IEEE DOI
2105
Image segmentation, Computed tomography, Semantics, Surgery,
Network architecture, Planning, Pattern recognition,
LBC
BibRef
Au-Yeung, L.[Lee],
Xie, X.H.[Xiang-Hua],
Chess, J.[James],
Scale, T.[Timothy],
Using Machine Learning to Refer Patients with Chronic Kidney Disease
to Secondary Care,
ICPR21(10219-10226)
IEEE DOI
2105
Support vector machines, Interpolation, Sensitivity, Training data,
Machine learning, Blood, Medical diagnostic imaging
BibRef
Shehata, M.,
Ghazal, M.,
Khalifeh, H.A.,
Khalil, A.,
Shalaby, A.,
Dwyer, A.C.,
Bakr, A.M.,
Keynton, R.,
El-Baz, A.,
A Deep Learning-Based CAD System For Renal Allograft Assessment:
Diffusion, Bold, And Clinical Biomarkers,
ICIP20(355-359)
IEEE DOI
2011
Kidney, Image segmentation, Biomarkers, Encoding,
Magnetic resonance imaging, Machine learning,
SAEs
BibRef
Bazgir, O.,
Barck, K.,
Carano, R.A.D.,
Weimer, R.M.,
Xie, L.,
Kidney segmentation using 3D U-Net localized with Expectation
Maximization,
SSIAI20(22-25)
IEEE DOI
2009
biomedical MRI, convolutional neural nets, diseases,
image segmentation, kidney, medical image processing,
Convolutional Neural Network
BibRef
Shehata, M.,
Shalaby, A.,
Ghazal, M.,
El-Ghar, M.A.,
Badawy, M.A.,
Beache, G.,
Dwyer, A.,
El-Melegy, M.,
Giridharan, G.,
Keynton, R.,
El-Baz, A.,
Early Assessment of Renal Transplants Using BOLD-MRI:
Promising Results,
ICIP19(1395-1399)
IEEE DOI
1910
Renal transplants, BOLD-MRI, mean R2*, pixel-wise R2*, machine learning
BibRef
Wetzer, E.[Elisabeth],
Lindblad, J.[Joakim],
Sintorn, I.M.[Ida-Maria],
Hultenby, K.[Kjell],
Sladoje, N.[Nataša],
Towards Automated Multiscale Imaging and Analysis in TEM:
Glomerulus Detection by Fusion of CNN and LBP Maps,
BioIm18(VI:465-475).
Springer DOI
1905
BibRef
Mathur, P.,
Ayyar, M.,
Shah, R.R.,
Sharma, S.,
Exploring Classification of Histological Disease Biomarkers From
Renal Biopsy Images,
WACV19(81-90)
IEEE DOI
1904
biological tissues, convolutional neural nets, diseases,
feature extraction, image classification, kidney,
Medical diagnostic imaging
BibRef
Abdeltawab, H.,
Shehatal, M.,
Shalaby, A.,
Mesbah, S.,
El-Baz, M.,
Ghazal, M.,
Al Khali, Y.,
Abou El-Ghar, M.,
Dwyer, A.C.,
El-Melegy, M.,
El-Baz, A.,
A New 3D CNN-based CAD System for Early Detection of Acute Renal
Transplant Rejection,
ICPR18(3898-3903)
IEEE DOI
1812
Kidney, Feature extraction,
Magnetic resonance imaging, Image segmentation,
3D CNN
BibRef
Yang, G.Y.[Guan-Yu],
Li, G.Q.[Guo-Qing],
Pan, T.[Tan],
Kong, Y.Y.[You-Yong],
Wu, J.S.[Jia-Song],
Shu, H.Z.[Hua-Zhong],
Luo, L.M.[Li-Min],
Dillenseger, J.L.[Jean-Louis],
Coatrieux, J.L.[Jean-Louis],
Tang, L.J.[Li-Jun],
Zhu, X.M.[Xiao-Mei],
Automatic Segmentation of Kidney and Renal Tumor in CT Images Based
on 3D Fully Convolutional Neural Network with Pyramid Pooling Module,
ICPR18(3790-3795)
IEEE DOI
1812
Tumors, Kidney, Computed tomography, Image segmentation,
pyramid pooling
BibRef
Shehata, M.,
Ghazal, M.,
Beache, G.,
El-Ghar, M.A.,
Dwyer, A.,
Hajjdiab, H.,
Khalil, A.,
El-Baz, A.,
Role of Integrating Diffusion MR Image-Markers with
Clinical-Biomarkers For Early Assessment of Renal Transplants,
ICIP18(146-150)
IEEE DOI
1809
Kidney, Diseases, Image segmentation,
Magnetic resonance imaging, Machine learning, Radio frequency,
Deep Learning
BibRef
Gadermayr, M.[Michael],
Klinkhammer, B.M.[Barbara Mara],
Boor, P.[Peter],
Merhof, D.[Dorit],
Do We Need Large Annotated Training Data for Detection Applications in
Biomedical Imaging? A Case Study in Renal Glomeruli Detection,
MLMI16(18-26).
Springer DOI
1611
BibRef
Hussain, M.A.[Mohammad Arafat],
Hamarneh, G.[Ghassan],
O'Connell, T.W.[Timothy W.],
Mohammed, M.F.[Mohammed F.],
Abugharbieh, R.[Rafeef],
Segmentation-Free Estimation of Kidney Volumes in CT with Dual
Regression Forests,
MLMI16(156-163).
Springer DOI
1611
BibRef
Shehata, M.[Mohamed],
Khalifa, F.[Fahmi],
Soliman, A.[Ahmed],
Alrefai, R.[Rahaf],
El-Ghar, M.A.[Mohamed Abou],
Dwyer, A.C.[Amy C.],
Ouseph, R.[Rosemary],
El-Baz, A.[Ayman],
A level set-based framework for 3D kidney segmentation from diffusion
MR images,
ICIP15(4441-4445)
IEEE DOI
1512
Adaptive Shape; DW-MRI; Deformable Model
BibRef
Khalifa, F.,
Soliman, A.,
Dwyer, A.C.,
Gimel'farb, G.,
El-Baz, A.,
A random forest-based framework for 3D kidney segmentation from
dynamic contrast-enhanced CT images,
ICIP16(3399-3403)
IEEE DOI
1610
Computed tomography
BibRef
Dai, G.Y.[Gao-Yuan],
Li, Z.C.[Zhi-Cheng],
Gu, J.[Jia],
Wang, L.[Lei],
Li, X.M.[Xing-Min],
Segmentation of kidneys from computed tomography using 3D fast
GrowCut algorithm,
ICIP13(1144-1147)
IEEE DOI
1402
Accuracy
BibRef
Landgren, M.[Matilda],
Sjöstrand, K.[Karl],
Ohlsson, M.[Mattias],
Ståhl, D.[Daniel],
Overgaard, N.C.[Niels Christian],
Åström, K.[Kalle],
Sixt, R.[Rune],
Edenbrandt, L.[Lars],
An Automated System for the Detection and Diagnosis of Kidney Lesions
in Children from Scintigraphy Images,
SCIA11(489-500).
Springer DOI
1105
BibRef
Khalifa, F.,
Gimel'farb, G.L.,
El-Ghar, M.A.[M. Abo],
Sokhadze, G.,
Manning, S.,
McClure, P.,
Ouseph, R.,
El-Baz, A.,
A new deformable model-based segmentation approach for accurate
extraction of the kidney from abdominal CT images,
ICIP11(3393-3396).
IEEE DOI
1201
BibRef
Schüffler, P.[Peter],
Ulas, A.[Aydin],
Castellani, U.[Umberto],
Murino, V.[Vittorio],
A Multiple Kernel Learning Algorithm for Cell Nucleus Classification of
Renal Cell Carcinoma,
CIAP11(I: 413-422).
Springer DOI
1109
BibRef
Strawn, N.[Nathaniel],
Yao, J.H.[Jian-Hua],
Tracking kidney tumor dimensional measurements via image morphing,
ICIP10(1721-1724).
IEEE DOI
1009
BibRef
Pan, T.,
Yang, G.,
Wang, C.,
Lu, Z.,
Zhou, Z.,
Kong, Y.,
Tang, L.,
Zhu, X.,
Dillenseger, J.,
Shu, H.,
Coatrieux, J.,
A Multi-Task Convolutional Neural Network for Renal Tumor
Segmentation and Classification Using Multi-Phasic CT Images,
ICIP19(809-813)
IEEE DOI
1910
Convolution neural network, multi-task, semantic segmentation,
medical image processing
BibRef
Gadermayr, M.[Michael],
Cooper, S.S.[Sean Steven],
Klinkhammer, B.M.[Barbara Mara],
Boor, P.[Peter],
Merhof, D.[Dorit],
A Quantitative Assessment of Image Normalization for Classifying
Histopathological Tissue of the Kidney,
GCPR17(3-13).
Springer DOI
1711
BibRef
Abd el Munim, H.E.,
Farag, A.A.[Aly A.],
Miller, W.,
AboelGhar, M.[Mohamed],
A kidney segmentation approach from DCE-MRI using level sets,
MMBIA08(1-6).
IEEE DOI
0806
BibRef
Boukerroui, D.[Djamal],
Touhami, W.[Wala],
Cocquerez, J.P.[Jean Pierre],
Automatic regions of interest identification and classification in CT
images: Application to kidney cysts,
IPTA08(1-8).
IEEE DOI
0811
BibRef
Koh, H.K.,
Shen, W.J.[Wei-Jia],
Shuter, B.,
Kassim, A.A.,
Segmentation of Kidney Cortex in MRI Studies using a Constrained
Morphological 3D H-maxima Transform,
ICARCV06(1-5).
IEEE DOI
0612
BibRef
Touhami, W.,
Boukerroui, D.,
Cocquerez, J.P.,
A Statistical Approach for Automatic Kidneys Detection,
ICIP05(III: 740-743).
IEEE DOI
0512
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Pancreatic Disease, CAT Analysis .