| 1. |
- Doyle, Scott, et al.
(författare)
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Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology
- 2010
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Ingår i: From Nano to Macro. - 9781424441266 ; , s. 1313-1316
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Konferensbidrag (refereegranskat)abstract
- A single digital pathology image can occupy over 10 gigabytes of hard disk space, rendering it difficult to store, analyze, and transmit. Though image compression provides a means of reducing the storage requirement, its effects on CAD (and pathologist) performance are not yet clear. In this work we assess the impact of compression on the ability of a CAD system to detect carcinoma of the prostate (CaP) in histological sections. The CAD algorithm proceeds as follows: Glands in the tissue are segmented using a region-growing algorithm. The size of each gland is then extracted and modeled using a mixture of Gamma distributions. A Markov prior (specifically, a probabilistic pairwise Markov model) is employed to encourage nearby glands to share the same class (i.e. cancerous or non-cancerous). Finally, cancerous glands are aggregated into continuous regions using a distance-hull algorithm. We evaluate CAD performance over 12 images compressed at 14 different compression ratios using JPEG2000. Algorithm performance (measured using the under the receiver operating characteristic curves) remains relatively constant for compression ratios up to 1:256. After this point performance degrades precipitously. We also have an expert pathologist view the compressed images and assign a confidence measure as to their diagnostic fidelity.
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| 2. |
- Doyle, Scott, et al.
(författare)
-
Evaluation of effects of JPEG2000 compression on a computer-aided detection system for prostate cancer on digitized histopathology
- 2010
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Ingår i: Biomedical Imaging. ; , s. 1313-1316
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Konferensbidrag (refereegranskat)abstract
- A single digital pathology image can occupy over 10 gigabytes of hard disk space, rendering it difficult to store, analyze, and transmit. Though image compression provides a means of reducing the storage requirement, its effects on computer-aided diagnosis (CAD) and pathologist performance are not yet clear. In this work we assess the impact of compression on the ability of a CAD system to detect carcinoma of the prostate (CaP) on histological sections. The CAD algorithm proceeds as follows: Glands in the tissue are segmented using a region-growing algorithm, and the size of each gland is extracted. A Markov prior (specifically, a probabilistic pairwise Markov model) is employed to encourage nearby glands to share the same class (i.e. cancerous or non-cancerous). Finally, cancerous glands are aggregated into continuous regions using a distancehull algorithm. We trained the CAD system on 28 images of wholemount histology (WMH) and evaluated performance on 12 images compressed at 14 different compression ratios (a total of 168 experiments) using JPEG2000. Algorithm performance (measured using the under the receiver operating characteristic curves) remains relatively constant for compression ratios up to1 :256, beyond which performance degrades precipitously. For completeness we also have an expert pathologist view a randomly-selected set of compressed images from one of the whole mount studies and assign a confidence measure as to their diagnostic fidelity. Pathologist confidence declined with increasing compression ratio as the information necessary to diagnose the sample was lost, dropping from 100% confidence at ratio 1:64 to 0% at ratio 1:8192.
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| 3. |
- Kårsnäs, Andreas, 1977-
(författare)
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Image Analysis Methods and Tools for Digital Histopathology Applications Relevant to Breast Cancer Diagnosis
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In 2012, more than 1.6 million new cases of breast cancer were diagnosed and about half a million women died of breast cancer. The incidence has increased in the developing world. The mortality, however, has decreased. This is thought to partly be the result of advances in diagnosis and treatment. Studying tissue samples from biopsies through a microscope is an important part of diagnosing breast cancer. Recent techniques include camera-equipped microscopes and whole slide scanning systems that allow for digital high-throughput scanning of tissue samples. The introduction of digital pathology has simplified parts of the analysis, but manual interpretation of tissue slides is still labor intensive and costly, and involves the risk for human errors and inconsistency. Digital image analysis has been proposed as an alternative approach that can assist the pathologist in making an accurate diagnosis by providing additional automatic, fast and reproducible analyses. This thesis addresses the automation of conventional analyses of tissue, stained for biomarkers specific for the diagnosis of breast cancer, with the purpose of complementing the role of the pathologist. In order to quantify biomarker expression, extraction and classification of sub-cellular structures are needed. This thesis presents a method that allows for robust and fast segmentation of cell nuclei meeting the need for methods that are accurate despite large biological variations and variations in staining. The method is inspired by sparse coding and is based on dictionaries of local image patches. It is implemented in a tool for quantifying biomarker expression of various sub-cellular structures in whole slide images. Also presented are two methods for classifying the sub-cellular localization of staining patterns, in an attempt to automate the validation of antibody specificity, an important task within the process of antibody generation. In addition, this thesis explores methods for evaluation of multimodal data. Algorithms for registering consecutive tissue sections stained for different biomarkers are evaluated, both in terms of registration accuracy and deformation of local structures. A novel region-growing segmentation method for multimodal data is also presented. In conclusion, this thesis presents computerized image analysis methods and tools of potential value for digital pathology applications.
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| 4. |
- Litjens, Geert, et al.
(författare)
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Evaluation of prostate segmentation algorithms for MRI : The PROMISE12 challenge
- 2014
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Ingår i: Medical Image Analysis. - : Elsevier BV. - 1361-8415 .- 1361-8423. ; 18:2, s. 359-373
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Tidskriftsartikel (refereegranskat)abstract
- Prostate MRI image segmentation has been an area of intense research due to the increased use of MRI as a modality for the clinical workup of prostate cancer. Segmentation is useful for various tasks, e.g. to accurately localize prostate boundaries for radiotherapy or to initialize multi-modal registration algorithms. In the past, it has been difficult for research groups to evaluate prostate segmentation algorithms on multi-center, multi-vendor and multi-protocol data. Especially because we are dealing with MR images, image appearance, resolution and the presence of artifacts are affected by differences in scanners and/or protocols, which in turn can have a large influence on algorithm accuracy. The Prostate MR Image Segmentation (PROMISE12) challenge was setup to allow a fair and meaningful comparison of segmentation methods on the basis of performance and robustness. In this work we will discuss the initial results of the online PROMISE12 challenge, and the results obtained in the live challenge workshop hosted by the MICCAI2012 conference. In the challenge, 100 prostate MR cases from 4 different centers were included, with differences in scanner manufacturer, field strength and protocol. A total of 11 teams from academic research groups and industry participated. Algorithms showed a wide variety in methods and implementation, including active appearance models, atlas registration and level sets. Evaluation was performed using boundary and volume based metrics which were combined into a single score relating the metrics to human expert performance. The winners of the challenge where the algorithms by teams Imorphics and ScrAutoProstate, with scores of 85.72 and 84.29 overall. Both algorithms where significantly better than all other algorithms in the challenge (p < 0.05) and had an efficient implementation with a run time of 8 min and 3 s per case respectively. Overall, active appearance model based approaches seemed to outperform other approaches like multi-atlas registration, both on accuracy and computation time. Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, we showed that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained. All results are available online at http://promise12.grand-challenge.org/. (C) 2013 Elsevier B.V. All rights reserved.
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