Optimized JPEG 2000 Compression for Efficient Storage of Histopathological Whole-Slide Images

Helin, H; Tolonen, Teemu; Ylinen, Onni; Tolonen, P.; Näpänkangas, Juha; Isola, Jorma

doi:10.4103/jpi.jpi_69_17

Cited by 27 publications

(15 citation statements)

References 12 publications

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“…21 With the use of the JPEG 2000 algorithm, it also becomes possible to store different parts of the same picture with different qualities, which makes it attractive for the compression of WSIs, 22 since ∼80% of a WSI area contains an empty (white) background region 23 that does not contain any tissue. Helin et al 24 showed that applying a very high degree of JPEG 2000 compression on the background part of WSIs and applying a conventional amount of compression (e.g., 35:1) on the tissue-containing part results in a high overall compression ratio. Compression gains of up to a factor 3 are reported compared to classical, nonadaptive compression with JPEG 2000.…”

Section: Introductionmentioning

confidence: 99%

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Zanjani

Zinger

Piepers³

et al. 2019

J. Med. Imag.

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The availability of massive amounts of data in histopathological whole-slide images (WSIs) has enabled the application of deep learning models and especially convolutional neural networks (CNNs), which have shown a high potential for improvement in cancer diagnosis. However, storage and transmission of large amounts of data such as gigapixel histopathological WSIs are challenging. Exploiting lossy compression algorithms for medical images is controversial but, as long as the clinical diagnosis is not affected, is acceptable. We study the impact of JPEG 2000 compression on our proposed CNN-based algorithm, which has produced performance comparable to that of pathologists and which was ranked second place in the CAMELYON17 challenge. Detecting tumor metastases in hematoxylin and eosin-stained tissue sections of breast lymph nodes is evaluated and compared with the pathologists' diagnoses in three different experimental setups. Our experiments show that the CNN model is robust against compression ratios up to 24:1 when it is trained on uncompressed high-quality images. We demonstrate that a model trained on lower quality images-i.e., lossy compressed images-depicts a classification performance that is significantly improved for the corresponding compression ratio. Moreover, it is also observed that the model performs equally well on all higher-quality images. These properties will help to design cloud-based computer-aided diagnosis (CAD) systems, e.g., telemedicine that employ deep CNN models that are more robust to image quality variations due to compression required to address data storage and transmission constraints. However, the results presented are specific to the CAD system and application described, and further work is needed to examine whether they generalize to other systems and applications.

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Section: Introductionmentioning

confidence: 99%

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Zanjani

Zinger

Piepers³

et al. 2019

J. Med. Imag.

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“…To our knowledge, most WSI slide-scanner manufactures use 1 of these 2 approaches as the backbone of their proprietary formats. 19 , 30 , 31 As a result, their study is most likely to be relevant in the storage and transmission of WSI images. That said, as more powerful novel compression schemes are introduced, 32 tested, and routinely used in practice, future work will be required to evaluate their respective compression artifacts.…”

Section: Discussionmentioning

confidence: 99%

“…JPEG and JPEG2000 20 lossy compression approaches were used in this study because they are most commonly used by scanner manufacturers and WSI formats. 19 JPEG allows the user to specify the desired level of compression via a quality score associated with the quantization of frequencies in the image. When employing JPEG2000, the user specifies a PSNR value, which results in the truncation of certain frequencies after a wavelet transformation.…”

Section: Methodsmentioning

confidence: 99%

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

Chen

Janowczyk

Madabhushi

2020

JCO Clinical Cancer Informatics

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PURPOSE Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored. METHODS We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.

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“…However, the provided metrics will enable us to objectively compare further system configurations, additional viewers and scanners, to drive the development toward a seamless DP workflow. For example, further improvements of digital viewers that were not discussed in this study include pretiling of WSI, new image compression techniques 23,24 and advanced Web streaming. 25,26 With improved digital systems, we hypothesize, digital sign out will be on par or even more efficient than with a microscope.…”

Section: Influence Of Tile Size On Time Per Tile and Tfovmentioning

confidence: 99%

Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology

Schüffler

Stamelos

Ahmed

et al. 2022

Archives of Pathology &Amp; Laboratory Medicine

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Context.— Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined. Objective.— To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers. Design.— With a universal slide viewer used in clinical routine diagnostics, we evaluate the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels. Results.— Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression. Conclusions.— This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.

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Optimized JPEG 2000 Compression for Efficient Storage of Histopathological Whole-Slide Images

Cited by 27 publications

References 12 publications

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Impact of JPEG 2000 compression on deep convolutional neural networks for metastatic cancer detection in histopathological images

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis

Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology

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