Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation
- URL: http://arxiv.org/abs/2501.14013v1
- Date: Thu, 23 Jan 2025 18:45:24 GMT
- Title: Leveraging Multiphase CT for Quality Enhancement of Portal Venous CT: Utility for Pancreas Segmentation
- Authors: Xinya Wang, Tejas Sudharshan Mathai, Boah Kim, Ronald M. Summers,
- Abstract summary: Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases.
Prior approaches have targeted the quality improvement of one specific CT phase.
A 3D progressive fusion and non-local (PFNL) network was developed to enhance the quality of the portal venous phase.
- Score: 8.279931474338541
- License:
- Abstract: Multiphase CT studies are routinely obtained in clinical practice for diagnosis and management of various diseases, such as cancer. However, the CT studies can be acquired with low radiation doses, different scanners, and are frequently affected by motion and metal artifacts. Prior approaches have targeted the quality improvement of one specific CT phase (e.g., non-contrast CT). In this work, we hypothesized that leveraging multiple CT phases for the quality enhancement of one phase may prove advantageous for downstream tasks, such as segmentation. A 3D progressive fusion and non-local (PFNL) network was developed. It was trained with three degraded (low-quality) phases (non-contrast, arterial, and portal venous) to enhance the quality of the portal venous phase. Then, the effect of scan quality enhancement was evaluated using a proxy task of pancreas segmentation, which is useful for tracking pancreatic cancer. The proposed approach improved the pancreas segmentation by 3% over the corresponding low-quality CT scan. To the best of our knowledge, we are the first to harness multiphase CT for scan quality enhancement and improved pancreas segmentation.
Related papers
- Multimodal Learning With Intraoperative CBCT & Variably Aligned Preoperative CT Data To Improve Segmentation [0.21847754147782888]
Cone-beam computed tomography (CBCT) is an important tool facilitating computer aided interventions.
While the degraded image quality can affect downstream segmentation, the availability of high quality, preoperative scans represents potential for improvements.
We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect of CBCT quality and misalignment on the final segmentation performance.
arXiv Detail & Related papers (2024-06-17T15:31:54Z) - A Unified Multi-Phase CT Synthesis and Classification Framework for
Kidney Cancer Diagnosis with Incomplete Data [18.15801599933636]
We propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT.
It simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images.
The proposed framework is based on fully 3D convolutional neural networks.
arXiv Detail & Related papers (2023-12-09T11:34:14Z) - A Cascaded Approach for ultraly High Performance Lesion Detection and
False Positive Removal in Liver CT Scans [15.352636778576171]
Liver cancer has high morbidity and mortality rates in the world.
Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow.
In this work, we customize a multi-object labeling tool for multi-phase CT images.
arXiv Detail & Related papers (2023-06-28T09:11:34Z) - Moving from 2D to 3D: volumetric medical image classification for rectal
cancer staging [62.346649719614]
preoperative discrimination between T2 and T3 stages is arguably both the most challenging and clinically significant task for rectal cancer treatment.
We present a volumetric convolutional neural network to accurately discriminate T2 from T3 stage rectal cancer with rectal MR volumes.
arXiv Detail & Related papers (2022-09-13T07:10:14Z) - CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for
Non-Contrast to Contrast CT Translation [56.622832383316215]
We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans.
Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran.
Our empirical results show that CyTran outperforms all competing methods.
arXiv Detail & Related papers (2021-10-12T23:25:03Z) - Explainability Guided Multi-Site COVID-19 CT Classification [79.4957965474334]
The limited number of supervised positive cases, the lack of region-based supervision, and the variability across acquisition sites are addressed.
Compared to the current state of the art, we obtain an increase of five percent in the F1 score on a site with a relatively high number of cases, and a gap twice as large for a site with much fewer training images.
arXiv Detail & Related papers (2021-03-25T08:56:08Z) - Multitask 3D CBCT-to-CT Translation and Organs-at-Risk Segmentation
Using Physics-Based Data Augmentation [4.3971310109651665]
In current clinical practice, noisy and artifact-ridden weekly cone-beam computed tomography (CBCT) images are only used for patient setup during radiotherapy.
Treatment planning is done once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures.
If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment and for deriving biomarkers for treatment response.
arXiv Detail & Related papers (2021-03-09T19:51:44Z) - Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas
Segmentation: Application to Automatic Pathological Liver CT Segmentation [4.262342157729123]
Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning.
Currently, the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications.
We propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images.
arXiv Detail & Related papers (2021-02-24T04:09:39Z) - Co-Heterogeneous and Adaptive Segmentation from Multi-Source and
Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion
Segmentation [48.504790189796836]
We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe)
We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling.
CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
arXiv Detail & Related papers (2020-05-27T06:58:39Z) - Synergistic Learning of Lung Lobe Segmentation and Hierarchical
Multi-Instance Classification for Automated Severity Assessment of COVID-19
in CT Images [61.862364277007934]
We propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images.
A multi-task deep network (called M$2$UNet) is then developed to assess the severity of COVID-19 patients.
Our M$2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment.
arXiv Detail & Related papers (2020-05-08T03:16:15Z) - Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via
Alignment Ensemble [77.5625174267105]
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture.
We suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection.
arXiv Detail & Related papers (2020-03-18T19:06:27Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.