Literature DB >> 26736926

Computer aided analysis of prostate histopathology images Gleason grading especially for Gleason score 7.

Jian Ren, Evita T Sadimin, Daihou Wang, Jonathan I Epstein, David J Foran, Xin Qi.   

Abstract

Clinically, prostate adenocarcinoma is diagnosed by recognizing certain morphology on histology. While the Gleason grading system has been shown to be the strongest prognostic factor for men with prostrate adenocarcinoma, there is a significant intra and interobserver variability between pathologists in assigning this grading system. In this study, we present a new method for prostate gland segmentation from which we then utilize to develop a computer aided Gleason grading. The novelty of our method is a region-based nuclei segmentation to get individual gland without using lumen as prior information. Because each gland region is surrounded by nuclei, individual gland can be segmented by using the structure features and Delaunay Triangulation. The precision, recal and F1 of this approach are 0.94±0.11, 0.60±0.23 and 0.70±0.19 respectively. Our method achieves a high accuracy for prostate gland segmentation with less computation time.

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Mesh:

Year:  2015        PMID: 26736926      PMCID: PMC4917302          DOI: 10.1109/EMBC.2015.7319026

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  10 in total

1.  Interobserver reproducibility of Gleason grading of prostatic carcinoma: urologic pathologists.

Authors:  W C Allsbrook; K A Mangold; M H Johnson; R B Lane; C G Lane; M B Amin; D G Bostwick; P A Humphrey; E C Jones; V E Reuter; W Sakr; I A Sesterhenn; P Troncoso; T M Wheeler; J I Epstein
Journal:  Hum Pathol       Date:  2001-01       Impact factor: 3.466

2.  Prostate cancer grading: use of graph cut and spatial arrangement of nuclei.

Authors:  Kien Nguyen; Anindya Sarkar; Anil K Jain
Journal:  IEEE Trans Med Imaging       Date:  2014-07-10       Impact factor: 10.048

3.  Structure and context in prostatic gland segmentation and classification.

Authors:  Kien Nguyen; Anindya Sarkar; Anil K Jain
Journal:  Med Image Comput Comput Assist Interv       Date:  2012

4.  COMPUTER-AIDED GLEASON GRADING OF PROSTATE CANCER HISTOPATHOLOGICAL IMAGES USING TEXTON FORESTS.

Authors:  Parmeshwar Khurd; Claus Bahlmann; Peter Maday; Ali Kamen; Summer Gibbs-Strauss; Elizabeth M Genega; John V Frangioni
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2010-04-17

5.  Gleason score 7 prostate cancer on needle biopsy: is the prognostic difference in Gleason scores 4 + 3 and 3 + 4 independent of the number of involved cores?

Authors:  Danil V Makarov; Harriete Sanderson; Alan W Partin; Jonathan I Epstein
Journal:  J Urol       Date:  2002-06       Impact factor: 7.450

6.  High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

Authors:  James P Monaco; John E Tomaszewski; Michael D Feldman; Ian Hagemann; Mehdi Moradi; Parvin Mousavi; Alexander Boag; Chris Davidson; Purang Abolmaesumi; Anant Madabhushi
Journal:  Med Image Anal       Date:  2010-04-29       Impact factor: 8.545

7.  Multiwavelet grading of pathological images of prostate.

Authors:  Kourosh Jafari-Khouzani; Hamid Soltanian-Zadeh
Journal:  IEEE Trans Biomed Eng       Date:  2003-06       Impact factor: 4.538

8.  Prognostic Gleason grade grouping: data based on the modified Gleason scoring system.

Authors:  Phillip M Pierorazio; Patrick C Walsh; Alan W Partin; Jonathan I Epstein
Journal:  BJU Int       Date:  2013-03-06       Impact factor: 5.588

9.  A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering.

Authors:  Juan Vidal; Gloria Bueno; John Galeotti; Marcial García-Rojo; Fernanda Relea; Oscar Déniz
Journal:  J Pathol Inform       Date:  2012-01-19

10.  Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer.

Authors:  Scott Doyle; Michael D Feldman; Natalie Shih; John Tomaszewski; Anant Madabhushi
Journal:  BMC Bioinformatics       Date:  2012-10-30       Impact factor: 3.169

  10 in total
  6 in total

Review 1.  Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies.

Authors:  Jacob Jordan; Jordan S Goldstein; David L Jaye; Metin Gurcan; Christopher R Flowers; Lee A D Cooper
Journal:  JCO Clin Cancer Inform       Date:  2018-02-09

2.  Gland segmentation in prostate histopathological images.

Authors:  Malay Singh; Emarene Mationg Kalaw; Danilo Medina Giron; Kian-Tai Chong; Chew Lim Tan; Hwee Kuan Lee
Journal:  J Med Imaging (Bellingham)       Date:  2017-06-21

3.  Predicting cancer outcomes from histology and genomics using convolutional networks.

Authors:  Pooya Mobadersany; Safoora Yousefi; Mohamed Amgad; David A Gutman; Jill S Barnholtz-Sloan; José E Velázquez Vega; Daniel J Brat; Lee A D Cooper
Journal:  Proc Natl Acad Sci U S A       Date:  2018-03-12       Impact factor: 11.205

4.  Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks.

Authors:  Jian Ren; Kubra Karagoz; Michael L Gatza; Eric A Singer; Evita Sadimin; David J Foran; Xin Qi
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-15

5.  Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images.

Authors:  Jian Ren; Ilker Hacihaliloglu; Eric A Singer; David J Foran; Xin Qi
Journal:  Front Bioeng Biotechnol       Date:  2019-05-15

6.  Statistical Analysis of Survival Models Using Feature Quantification on Prostate Cancer Histopathological Images.

Authors:  Jian Ren; Eric A Singer; Evita Sadimin; David J Foran; Xin Qi
Journal:  J Pathol Inform       Date:  2019-09-27
  6 in total

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