Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling

Nagoor, Omniah H.; Whittle, Joss; Deng, Jingjing; Mora, Benjamin; Jones, Mark W.

doi:10.1109/icip40778.2020.9191031

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…In this study, an ANN model was established to individually predict the efficacy of irinotecan‐based first‐line chemotherapy in CRC patients before chemotherapy. In the original images, we chose the 5 mm slice thickness, which can reduce the noise of the image and may have more reliable radiomics features 16 …”

Section: Discussionmentioning

confidence: 99%

“…In the original images, we chose the 5 mm slice thickness, which can reduce the noise of the image and may have more reliable radiomics features. 16 Three kinds of sampling sizes were used for the ROI data to ensure the integrity of feature extraction. After a multistep algorithm, 1×1×1, 3×3×3, 5×5×5 mm 3 each 11 radiomics features are finally retained, and the final retained radiomics features and clinical features were used as the input of the ANN.…”

Section: Discussionmentioning

confidence: 99%

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CT‐based radiomics for the identification of colorectal cancer liver metastases sensitive to first‐line irinotecan‐based chemotherapy

et al. 2023

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Background: Chemosensitivity prediction in colorectal cancer patients with liver metastases has remained a research hotspot. Radiomics can extract features from patient imaging, and deep learning or machine learning can be used to build models to predict patient outcomes prior to chemotherapy. Purpose: In this study, the radiomics features and clinical data of colorectal cancer patients with liver metastases were used to predict their sensitivity to irinotecan-based chemotherapy. Methods: A total of 116 patients with unresectable colorectal cancer liver metastases who received first-line irinotecan-based chemotherapy from January 2015 to January 2020 in our institution were retrospectively collected. Overall, 116 liver metastases were randomly divided into training (n = 81) and validation (n = 35) cohorts in a 7:3 ratio. The effect of chemotherapy was determined based on Response Evaluation Criteria in Solid Tumors. The lesions were divided into response and nonresponse groups. Regions of interest (ROIs) were manually segmented, and sample sizes of 1×1×1, 3×3×3, 5×5×5 mm 3 were used to extract radiomics features. The relevant features were identified through Pearson correlation analysis and the MRMR algorithm, and the clinical data were merged into the artificial neural network. Finally, the p-model was obtained after repeated learning and testing. Results: The p-model could distinguish responders in the training (area under the curve [AUC] 0.754, 95% CI 0.650-0.858) and validation cohorts (AUC 0.752 95% CI 0.581-0.904). AUC values of the pure image group model are 0.720 (95% CI 0.609-0.827) and 0.684 (95% CI 0.529-0.890) for the training and validation cohorts respectively. As for the clinical data model, AUC values of the training and validation cohorts are 0.638 (95% CI 0.500-0.757) and 0.545 (95% CI 0.360-0.785), respectively. The performances of the latter two are less than that of the former. Conclusion:The p-model has the potential to discriminate colorectal cancer patients sensitive to chemotherapy. This model holds promise as a noninvasive tool to predict the response of colorectal liver metastases to chemotherapy, allowing for personalized treatment planning. K E Y W O R D Sartificial neural networks, chemotherapy efficacy, colorectal cancer liver metastasis, irinotecan, machine learning, radiomics Wei Qi and Jing Yang contributed equally to this work.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

CT‐based radiomics for the identification of colorectal cancer liver metastases sensitive to first‐line irinotecan‐based chemotherapy

et al. 2023

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“…New lossless compression techniques for 16-bit medical image volumes are presented in paper [27]. The goal is to employ a trained neural network (NN) to predict 3D data in a way that minimizes discrepancies with the original values and mathematically codes the residuals to reduce their size.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques

Khalifeh,

Taghizadeh,

Ghanbarian

et al. 2023

Preprint

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In order to compress medical pictures for long-term storage, two methods are used in this work. The first step is to use a neural network–based categorization system to simplify images using a hierarchical modeling technique. The Huffman cipher is then used to compress the reduced images. In the second method, a deep neural network is trained to make predictions. This method can potentially reduce the amount of data needed to describe a picture by using a trained neural network to make intelligent guesses about the location of individual pixels. Huffman compression is used to encrypt the remaining data. By using an improved spatial filtering method to the picture data, we can decode it and then use meta-heuristic algorithms like gray wolf optimization (GWO) and wild horse optimization (WHO) to rebuild the image. Without sacrificing data compression efficacy, this paves the way for a more practical implementation of the proposed techniques in cases when outcomes are uncertain. Images can be simplified using the suggested approaches, leading to faster decoding. Afterwards, performance metrics were taken and evaluated following predetermined daily procedures. The suggested approaches outperformed state-of-the-art deep learning-based systems in compressing medical images while maintaining an exceptionally high quality level.

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“…Papers that focus on data compression are out of our survey's scope. Unlike model compression, data compression (i.e., text compression [19], genomic compression [20], and image compression [21][22][23]) forms a central role to handle the bottleneck of data storage, transmission, and processing.…”

Section: Survey Scopementioning

confidence: 99%

Literature Review of Deep Network Compression

2021

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Deep networks often possess a vast number of parameters, and their significant redundancy in parameterization has become a widely-recognized property. This presents significant challenges and restricts many deep learning applications, making the focus on reducing the complexity of models while maintaining their powerful performance. In this paper, we present an overview of popular methods and review recent works on compressing and accelerating deep neural networks. We consider not only pruning methods but also quantization methods, and low-rank factorization methods. This review also intends to clarify these major concepts, and highlights their characteristics, advantages, and shortcomings.

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Lossless Compression For Volumetric Medical Images Using Deep Neural Network With Local Sampling

Cited by 9 publications

References 17 publications

CT‐based radiomics for the identification of colorectal cancer liver metastases sensitive to first‐line irinotecan‐based chemotherapy

CT‐based radiomics for the identification of colorectal cancer liver metastases sensitive to first‐line irinotecan‐based chemotherapy

Medical image compression and restoration using a hybrid neural network combining Huffman and meta-heuristic techniques

Literature Review of Deep Network Compression

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