Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation

Li, Guoping; Zhang, Lin

doi:10.1016/j.jiph.2019.08.011

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…Utilizing the soybean root data, which had been annotated with cell type information, we applied SpaGCN software 19 to cluster the raw SRT data consisting of 27,878 genes into six clusters. The clustering results achieved an Adjusted Rand Index (ARI) 20,21 value of 0.49, Accuracy (ACC) value of 0.66, Normalized Mutual Information (NMI) 21,22 value of 0.54, and Fowlkes-Mallows Index (FMI) 21 value of 0.66 in Fig. 2e.…”

Section: Srt Data Contains Noise That Impedes Downstream Analysesmentioning

confidence: 99%

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Kang

Sun

et al. 2023

Preprint

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The recent advancements in spatially resolved transcriptomics (SRT) technology have enabled the acquisition of gene expression data at near- or sub-single-cell resolution, along with simultaneous imaging of physical locations. Nevertheless, necessary experimental procedures such as tissue fixation, permeabilization, and tissue removal inevitably induce the diffusion of transcribed molecules. Consequently, this leads to the partial capture of ex-situ transcripts in SRT data, thereby introducing a considerable amount of noise into the dataset. To address this issue, in this study, we focused on evaluating the diffusion pattern of individual genes within tissue regions and quantitatively calculating their signal-to-noise ratio (SNR). Through this analysis, we successfully identified "invalid genes" exhibiting widespread expression across tissue regions. Then by filtering out these genes, we effectively reduced the high noise level present in SRT data. To achieve this, we developed the gene filter denoising (GF) algorithm, which utilizes the optimal transport method to compute the gene diffusion coefficient and generate denoised SRT data. One notable advantage of our GF algorithm is its ability to fully "respect" the raw sequencing data, thereby avoiding the introduction of false positives often associated with traditional interpolation and modification denoising methods. Furthermore, we conducted comprehensive validation of GF, and the GFdenoised SRT data demonstrated substantial improvements in clustering, identification of differentially expressed genes (DEGs), and cell type annotation. Taken together, we believe that the GF denoising technique will serve as an essential and crucial step in exploring SRT data and investigating the underlying biological processes.

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Section: Srt Data Contains Noise That Impedes Downstream Analysesmentioning

confidence: 99%

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Kang

Sun

et al. 2023

Preprint

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“…GD judges the final registration result by measuring the change of low-frequency gray value due to gradient reduction. The formula is shown in Equations ( 5)- (7) in Appendix.…”

Section: The Normalized Mutual Information-gradient DI Erencementioning

confidence: 99%

“…At present, for evaluation of the results of the 2D–3D image registration, the selected similarity measurement function includes only the gray-level information of a single image and lacks the spatial information of the image as an aid, resulting in large errors in the results of the final similarity evaluation, such as Normalized Mutual (NM) and Normalized Mutual Information (NMI) ( 6 , 7 ). In the 2D–3D registration process, the selected optimization algorithm considers only the optimal value and it does not consider improving the registration efficiency, such as Powell and Gradient Descent (GD) ( 8 , 9 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the depth perception of DSA images with 2D–3D registration

Zhang

Deng²,

Tian³

et al. 2023

Front. Neurol.

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ObjectiveToday, cerebrovascular disease has become an important health hazard. Therefore, it is necessary to perform a more accurate and less time-consuming registration of preoperative three-dimensional (3D) images and intraoperative two-dimensional (2D) projection images which is very important for conducting cerebrovascular disease interventions. The 2D–3D registration method proposed in this study is designed to solve the problems of long registration time and large registration errors in 3D computed tomography angiography (CTA) images and 2D digital subtraction angiography (DSA) images.MethodsTo make a more comprehensive and active diagnosis, treatment and surgery plan for patients with cerebrovascular diseases, we propose a weighted similarity measure function, the normalized mutual information-gradient difference (NMG), which can evaluate the 2D–3D registration results. Then, using a multi-resolution fusion optimization strategy, the multi-resolution fused regular step gradient descent optimization (MR-RSGD) method is presented to attain the optimal value of the registration results in the process of the optimization algorithm.ResultIn this study, we adopt two datasets of the brain vessels to validate and obtain similarity metric values which are 0.0037 and 0.0003, respectively. Using the registration method proposed in this study, the time taken for the experiment was calculated to be 56.55s and 50.8070s, respectively, for the two sets of data. The results show that the registration methods proposed in this study are both better than the Normalized Mutual (NM) and Normalized Mutual Information (NMI).ConclusionThe experimental results in this study show that in the 2D–3D registration process, to evaluate the registration results more accurately, we can use the similarity metric function containing the image gray information and spatial information. To improve the efficiency of the registration process, we can choose the algorithm with gradient optimization strategy. Our method has great potential to be applied in practical interventional treatment for intuitive 3D navigation.

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“…This depends on the expert's medical knowledge and experience and has strong subjectivity. As a result, different experts may have different conclusions [ 7 ]. Additionally, manual segmentation is time-consuming, labor-intensive, and unable to deal with large amount medical information.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope

Zhao

2021

Contrast Media & Molecular Imaging

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The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant ( P < 0.05 ). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference ( P > 0.05 ). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications ( P > 0.05 ). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy.

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Localization of prostatic tumor’s infection based on normalized mutual information MRI image segmentation

Cited by 5 publications

References 11 publications

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics Data

Enhancing the depth perception of DSA images with 2D–3D registration

Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope

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