Learned Lossy Image Compression for Volumetric Medical Data

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Learned Lossy Image Compression for Volumetric Medical Data

Popis výsledku v původním jazyce

This work addresses the problem of lossy compression of volumetric images consisting of individual slices such as those produced by CT scans and MRI machines in medical imaging. We propose an extension of a single-image lossy compression method with an autoregressive context module to a sequential encoding of the volumetric slices. In particular, we remove the intra-slice autoregressive relation and instead condition the entropy model of the latent on the previous slice in the sequence. This modification alleviates the typical disadvantages of autoregressive contexts and leads to a significant increase in performance compared to encoding each slice independently. We test the proposed method on a dataset of diverse CT scan images in a setting with an emphasis on high-fidelity reconstruction required in medical imaging and show that it compares favorably against several established state-of-the-art codecs in both performance and runtime.

Název v anglickém jazyce

Learned Lossy Image Compression for Volumetric Medical Data

Popis výsledku anglicky

This work addresses the problem of lossy compression of volumetric images consisting of individual slices such as those produced by CT scans and MRI machines in medical imaging. We propose an extension of a single-image lossy compression method with an autoregressive context module to a sequential encoding of the volumetric slices. In particular, we remove the intra-slice autoregressive relation and instead condition the entropy model of the latent on the previous slice in the sequence. This modification alleviates the typical disadvantages of autoregressive contexts and leads to a significant increase in performance compared to encoding each slice independently. We test the proposed method on a dataset of diverse CT scan images in a setting with an emphasis on high-fidelity reconstruction required in medical imaging and show that it compares favorably against several established state-of-the-art codecs in both performance and runtime.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 26th Computer Vision Winter Workshop (CVWW 2023)

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

9

Strana od-do

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Název nakladatele

CEUR-WS

Místo vydání

https://ceur-ws.org

Místo konání akce

Krems a.d. Donau

Datum konání akce

15. 2. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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