標(biāo)題: Titlebook: Bildverarbeitung für die Medizin 2023; Proceedings, German Thomas M. Deserno,Heinz Handels,Thomas Tolxdorff Conference proceedings 2023 De [打印本頁] 作者: metamorphose 時(shí)間: 2025-3-21 18:07
書目名稱Bildverarbeitung für die Medizin 2023影響因子(影響力)
書目名稱Bildverarbeitung für die Medizin 2023影響因子(影響力)學(xué)科排名
書目名稱Bildverarbeitung für die Medizin 2023網(wǎng)絡(luò)公開度
書目名稱Bildverarbeitung für die Medizin 2023網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bildverarbeitung für die Medizin 2023被引頻次
書目名稱Bildverarbeitung für die Medizin 2023被引頻次學(xué)科排名
書目名稱Bildverarbeitung für die Medizin 2023年度引用
書目名稱Bildverarbeitung für die Medizin 2023年度引用學(xué)科排名
書目名稱Bildverarbeitung für die Medizin 2023讀者反饋
書目名稱Bildverarbeitung für die Medizin 2023讀者反饋學(xué)科排名
作者: 能夠支付 時(shí)間: 2025-3-21 23:10 作者: SIT 時(shí)間: 2025-3-22 02:39
Die Gruppe der deutschen Jihadistenpathologies into the generated videos. Extension (i) will be shown to increase temporal consistency and realism of the generated GI endoscopy videos. Feasibility and potential of (ii) is illustrated with superficial and deep duodenal ulcer as conditional classes.作者: Suppository 時(shí)間: 2025-3-22 07:09 作者: Toxoid-Vaccines 時(shí)間: 2025-3-22 11:29
https://doi.org/10.1007/978-3-531-92409-0n both cases, our proposed label dependency approach improved the performance of the baseline models: the dice score (DS) of the ventral organ segmentation improved by more than 3.5 % and the vertebrae identification rate by 1.8%.作者: Petechiae 時(shí)間: 2025-3-22 16:21 作者: 參考書目 時(shí)間: 2025-3-22 17:53
Religiosity and Mental Health in Islamin terms of HU values, and performed dosimetry calculations on the original nCT and ceCT, and on the in-silico nCTs to evaluate the impact on the dose rate. The two approaches yielded good results both in terms of HU reduction (more than 30%) and in the difference of dose rate against the original nCT (less than 1.38% vs. 4.76%).作者: 單調(diào)女 時(shí)間: 2025-3-23 00:38 作者: 是剝皮 時(shí)間: 2025-3-23 05:07 作者: helper-T-cells 時(shí)間: 2025-3-23 09:09
Enhancing Medical Image Segmentation with Anatomy-aware Label Dependency,n both cases, our proposed label dependency approach improved the performance of the baseline models: the dice score (DS) of the ventral organ segmentation improved by more than 3.5 % and the vertebrae identification rate by 1.8%.作者: 鳴叫 時(shí)間: 2025-3-23 13:17
Improved Tractography by Means of DL-based DWI Image Enhancement,a substantially more accurate tractography with XTRACT. Our results show that the method increases the correlation of tracts extracted from a clinical dataset with the ones extracted from a high-quality one from 64% to 83%. Clinically, this implies that both more tracts are detected and that the details of detected tracts are higher.作者: bacteria 時(shí)間: 2025-3-23 16:54
Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT,in terms of HU values, and performed dosimetry calculations on the original nCT and ceCT, and on the in-silico nCTs to evaluate the impact on the dose rate. The two approaches yielded good results both in terms of HU reduction (more than 30%) and in the difference of dose rate against the original nCT (less than 1.38% vs. 4.76%).作者: Inflammation 時(shí)間: 2025-3-23 20:36
Automatic Vertebrae Segmentation in MR Volumes,ed three methods that were already established in the segmentation of CT images: 3D UNet as our baseline, an iterative binary segmentation approach, and a multi-stage segmentation approach. Our experiments achieved a mean Dice score of 88.1% and demonstrate that CT segmentation methods are easily transferable to MR segmentation.作者: obsession 時(shí)間: 2025-3-23 23:33
Conference proceedings 2023mentierung und Analyse, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinelles Lernens, der biomechanischen Modellierung sowie der Validierung und Qualit?tssicherung zum Einsatz.?.作者: 寄生蟲 時(shí)間: 2025-3-24 04:45
https://doi.org/10.1007/978-94-6300-779-5sel tree. Comparing our proposed model with the current state of the art for this task, a 2D U-Net operating on axial NCCT slices, we were able to slightly increase quantitative overlap metrics as well as achieve notably improved qualitative results w.r.t. spatial continuity of the segmented vessel tree.作者: 好開玩笑 時(shí)間: 2025-3-24 07:32 作者: insolence 時(shí)間: 2025-3-24 11:04 作者: 頭盔 時(shí)間: 2025-3-24 15:01 作者: 救護(hù)車 時(shí)間: 2025-3-24 19:39 作者: 表示向下 時(shí)間: 2025-3-25 00:41
Keynote: Beyond Supervised Learning,robust modelling of normal appearance and identification of features pointing into the long tail of population data. In this talk, I will explore the fitness of machine learning for applications at the front line of care and high throughput population health screening, specifically in prenatal healt作者: Complement 時(shí)間: 2025-3-25 05:25
Keynote: Fully Automated Bone Removal in CBCT of the Lower Body Stem,wer body stem, which includes the abdomen and pelvis, does not have a BRM for cone beam CT (CBCT). This frequently necessitates the interventionist doing a manual BRM, particularly in the pelvic area, as in the case of a prostate embolization, necessitating his exit from the interventional room.The 作者: 碎石頭 時(shí)間: 2025-3-25 07:39 作者: sebaceous-gland 時(shí)間: 2025-3-25 13:29 作者: acquisition 時(shí)間: 2025-3-25 16:55 作者: PAC 時(shí)間: 2025-3-25 22:44
Abstract: Shape-based Segmentation of Retinal Layers and Fluids in OCT Image Data,ecades. Based on its high-resolution cross-sectional images, OCT supports diagnosis of various eye diseases. For clinical examination and treatment planning, automated segmentation of individual retinal layers and pathologies is helpful. Retinal layers follow a strict topology that is not addressed 作者: 人類 時(shí)間: 2025-3-26 01:25 作者: exclamation 時(shí)間: 2025-3-26 07:04
Abstract: Liver Tumor Segmentation in Late-phase MRI using Multi-model Training and an Anisotropic on deep learning-based liver tumor segmentation have focused on contrast-enhanced CT, however dynamic contrastenhanced MRI (DCE-MRI) can yield a higher sensitivity. In this work , we demonstrate the deep learning-based segmentation of liver tumors in the late hepatocellular phase of DCE-MRI. In part作者: 爭(zhēng)吵加 時(shí)間: 2025-3-26 08:30
Automatic Vertebrae Segmentation in MR Volumes,ome popular in segmenting the spine in computed tomography (CT) volumes. However, few options have been tested for magnetic resonance (MR) imaging segmentation. In this paper, we provide a comparison of three deep learning methods tackling the automatic vertebrae segmentation in MR volumes.We select作者: judiciousness 時(shí)間: 2025-3-26 14:59 作者: 拘留 時(shí)間: 2025-3-26 18:40 作者: 圖表證明 時(shí)間: 2025-3-26 23:59
Enhancing Medical Image Segmentation with Anatomy-aware Label Dependency, body. However, a medical expert would include in their reasoning also the context around the organ. In this work, we propose reproducing this human behavior by enhancing the conventional multi-class segmentation pipeline with additional anatomical information. We apply this concept to a ventral org作者: ARENA 時(shí)間: 2025-3-27 03:42 作者: 溝通 時(shí)間: 2025-3-27 07:29
Planning of Spherical Volumes for Treating Renal Tumors by Thermal Ablation with Tissue Shrinkage Etumors, multiple overlapping ablations are imperative. Currently, there is limited software available that incorporates tissue shrinkage into planning for ablation therapy while predicting the lowest number of ablation zones with complete tumor coverage. In this work, a time-power-dependent model to作者: 推測(cè) 時(shí)間: 2025-3-27 10:09 作者: 共和國(guó) 時(shí)間: 2025-3-27 17:37 作者: Annotate 時(shí)間: 2025-3-27 20:31
Improved Tractography by Means of DL-based DWI Image Enhancement,ty between brain regions and planning operations on the brain. XTRACT is a recently developed program for automatically delineating 42 major WM tracts in high-quality DWI data. However, acquiring such data is time-consuming, limiting its clinical application. In this work, we propose using a deep ne作者: forthy 時(shí)間: 2025-3-28 00:48
Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT, is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork,we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches作者: amplitude 時(shí)間: 2025-3-28 06:08
Unsupervised Super Resolution in X-ray Microscopy using a Cycle-consistent Generative Model,cale due to its high spatial resolution and strong bone to soft tissue contrast. Although in-vivo imaging of bone structures on the micro scale is desired from a medical perspective, high radiation dose so-far prohibits imaging living animals. Research has been focused on generating high-quality rec作者: 現(xiàn)代 時(shí)間: 2025-3-28 08:19 作者: Lyme-disease 時(shí)間: 2025-3-28 12:51
Informatik aktuellhttp://image.papertrans.cn/b/image/186211.jpg作者: 2否定 時(shí)間: 2025-3-28 16:39
https://doi.org/10.1007/978-3-658-39285-7robust modelling of normal appearance and identification of features pointing into the long tail of population data. In this talk, I will explore the fitness of machine learning for applications at the front line of care and high throughput population health screening, specifically in prenatal healt作者: 逗它小傻瓜 時(shí)間: 2025-3-28 22:34
Diskussion der Ergebnisse und Ausblick,wer body stem, which includes the abdomen and pelvis, does not have a BRM for cone beam CT (CBCT). This frequently necessitates the interventionist doing a manual BRM, particularly in the pelvic area, as in the case of a prostate embolization, necessitating his exit from the interventional room.The 作者: CHIP 時(shí)間: 2025-3-28 23:55
Die Gruppe der deutschen Jihadistenppearance. Aiming at a hyperrealistic training environment, we propose a CycleGAN-based framework to translate the training videos into realistically appearing GI endoscopy videos. We build on the concept of tempCycleGAN and (i) extend it to a generic framework to simultaneously work on . subsequent作者: 胰臟 時(shí)間: 2025-3-29 03:39
Islamistischer Terrorismus in Deutschlandordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone beam computed tomography (CBCT) systems. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle for navigation. In this work, 作者: vasculitis 時(shí)間: 2025-3-29 10:30
https://doi.org/10.1007/978-3-658-42830-3itoring, and evaluating the surgical result. C-arm positioning is usually performed by hand, involving repeated or even continuous fluoroscopy at a cost of radiation exposure and time. We propose to automate this procedure and estimate the pose update for C-arm repositioning directly from a first X-作者: LUDE 時(shí)間: 2025-3-29 14:39
Islamistischer Terrorismus in Deutschlandecades. Based on its high-resolution cross-sectional images, OCT supports diagnosis of various eye diseases. For clinical examination and treatment planning, automated segmentation of individual retinal layers and pathologies is helpful. Retinal layers follow a strict topology that is not addressed 作者: Countermand 時(shí)間: 2025-3-29 15:51
Fragestellung und Erkenntnisinteresse back to 2018, nnU-Net continues to provide competitive out-of-the-box solutions for a broad variety of segmentation problems and is regularly used as a development framework for challenge-winning algorithms. Here, we use nnU-Net to participate in the AMOS-2022 challenge, which was a MICCAI22 challe作者: MULTI 時(shí)間: 2025-3-29 20:12
Islamistischer Terrorismus in Deutschlandon deep learning-based liver tumor segmentation have focused on contrast-enhanced CT, however dynamic contrastenhanced MRI (DCE-MRI) can yield a higher sensitivity. In this work , we demonstrate the deep learning-based segmentation of liver tumors in the late hepatocellular phase of DCE-MRI. In part作者: coltish 時(shí)間: 2025-3-30 01:05
https://doi.org/10.1007/978-3-658-07180-6ome popular in segmenting the spine in computed tomography (CT) volumes. However, few options have been tested for magnetic resonance (MR) imaging segmentation. In this paper, we provide a comparison of three deep learning methods tackling the automatic vertebrae segmentation in MR volumes.We select作者: 暫停,間歇 時(shí)間: 2025-3-30 04:12 作者: 絆住 時(shí)間: 2025-3-30 12:13
https://doi.org/10.1007/978-3-531-92409-0ration methods. Our method integrates a vessel segmentation network into the image-to-image translation task by extending the CycleGAN framework. The segmentation network is inserted prior to aUNet vision transformer generator network and serves as a shared representation between both domains. We re作者: 多余 時(shí)間: 2025-3-30 15:44
https://doi.org/10.1007/978-3-531-92409-0 body. However, a medical expert would include in their reasoning also the context around the organ. In this work, we propose reproducing this human behavior by enhancing the conventional multi-class segmentation pipeline with additional anatomical information. We apply this concept to a ventral org作者: 爭(zhēng)吵加 時(shí)間: 2025-3-30 17:34 作者: CORE 時(shí)間: 2025-3-30 23:26 作者: 不斷的變動(dòng) 時(shí)間: 2025-3-31 02:31
https://doi.org/10.1007/978-94-6300-779-5 stroke. We present a deep learning model to estimate the cerebral vessel tree from the NCCT instead of subsequently performed contrast-enhanced imaging techniques, e.g. computed tomography angiography (CTA). We employ a volumetric sliding window approach and feed the patches to a 3D U-Net. This U-N作者: 高度表 時(shí)間: 2025-3-31 06:07 作者: 周興旺 時(shí)間: 2025-3-31 11:10
Religiosity and Mental Health in Islamty between brain regions and planning operations on the brain. XTRACT is a recently developed program for automatically delineating 42 major WM tracts in high-quality DWI data. However, acquiring such data is time-consuming, limiting its clinical application. In this work, we propose using a deep ne作者: 一大塊 時(shí)間: 2025-3-31 16:26
Religiosity and Mental Health in Islam is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork,we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches作者: Condescending 時(shí)間: 2025-3-31 19:28 作者: 偽造 時(shí)間: 2025-3-31 22:37
Bildverarbeitung für die Medizin 2023978-3-658-41657-7Series ISSN 1431-472X Series E-ISSN 2628-8958 作者: 手勢(shì) 時(shí)間: 2025-4-1 04:52
https://doi.org/10.1007/978-3-658-41657-7Computerunterstützte Medizin; Computerunterstützte Diagnose; Visualisierung; Bildverarbeitung für die M作者: BYRE 時(shí)間: 2025-4-1 08:08
