113 research outputs found
GPU-based 3D iceball modeling for fast cryoablation simulation and planning
Purpose The elimination of abdominal tumors by percutaneous cryoablation has been shown to be an effective and less invasive alternative to open surgery. Cryoablation destroys malignant cells by freezing them with one or more cryoprobes inserted into the tumor through the skin. Alternating cycles of freezing and thawing produce an enveloping iceball that causes the tumor necrosis. Planning such a procedure is difficult and time-consuming, as it is necessary to plan the number and cryoprobe locations and predict the iceball shape which is also influenced by the presence of heating sources, e.g., major blood vessels and warm saline solution, injected to protect surrounding structures from the cold. Methods This paper describes a method for fast GPU-based iceball modeling based on the simulation of thermal propagation in the tissue. Our algorithm solves the heat equation within a cube around the cryoprobes tips and accounts for the presence of heating sources around the iceball. Results Experimental results of two studies have been obtained: an ex vivo warm gel setup and simulation on five retrospective patient cases of kidney tumors cryoablation with various levels of complexity of the vascular structure and warm saline solution around the tumor tissue. The experiments have been conducted in various conditions of cube size and algorithm implementations. Results show that it is possible to obtain an accurate result within seconds. Conclusion The promising results indicate that our method yields accurate iceball shape predictions in a short time and is suitable for surgical planning
Test-time augmentation-based active learning and self-training for label-efficient segmentation
Deep learning techniques depend on large datasets whose annotation is
time-consuming. To reduce annotation burden, the self-training (ST) and
active-learning (AL) methods have been developed as well as methods that
combine them in an iterative fashion. However, it remains unclear when each
method is the most useful, and when it is advantageous to combine them. In this
paper, we propose a new method that combines ST with AL using Test-Time
Augmentations (TTA). First, TTA is performed on an initial teacher network.
Then, cases for annotation are selected based on the lowest estimated Dice
score. Cases with high estimated scores are used as soft pseudo-labels for ST.
The selected annotated cases are trained with existing annotated cases and ST
cases with border slices annotations. We demonstrate the method on MRI fetal
body and placenta segmentation tasks with different data variability
characteristics. Our results indicate that ST is highly effective for both
tasks, boosting performance for in-distribution (ID) and out-of-distribution
(OOD) data. However, while self-training improved the performance of
single-sequence fetal body segmentation when combined with AL, it slightly
deteriorated performance of multi-sequence placenta segmentation on ID data. AL
was helpful for the high variability placenta data, but did not improve upon
random selection for the single-sequence body data. For fetal body segmentation
sequence transfer, combining AL with ST following ST iteration yielded a Dice
of 0.961 with only 6 original scans and 2 new sequence scans. Results using
only 15 high-variability placenta cases were similar to those using 50 cases.
Code is available at: https://github.com/Bella31/TTA-quality-estimation-ST-ALComment: Accepted to MICCAI MILLanD workshop 202
Automatic linear measurements of the fetal brain on MRI with deep neural networks
Timely, accurate and reliable assessment of fetal brain development is
essential to reduce short and long-term risks to fetus and mother. Fetal MRI is
increasingly used for fetal brain assessment. Three key biometric linear
measurements important for fetal brain evaluation are Cerebral Biparietal
Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter
(TCD), obtained manually by expert radiologists on reference slices, which is
time consuming and prone to human error. The aim of this study was to develop a
fully automatic method computing the CBD, BBD and TCD measurements from fetal
brain MRI. The input is fetal brain MRI volumes which may include the fetal
body and the mother's abdomen. The outputs are the measurement values and
reference slices on which the measurements were computed. The method, which
follows the manual measurements principle, consists of five stages: 1)
computation of a Region Of Interest that includes the fetal brain with an
anisotropic 3D U-Net classifier; 2) reference slice selection with a
Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation
with a multiclass U-Net classifier; 4) computation of the fetal brain
midsagittal line and fetal brain orientation, and; 5) computation of the
measurements. Experimental results on 214 volumes for CBD, BBD and TCD
measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm
respectively, and a Bland-Altman 95% confidence interval () of 3.92mm,
3.98mm and 2.25mm respectively. These results are similar to the manual
inter-observer variability. The proposed automatic method for computing
biometric linear measurements of the fetal brain from MR imaging achieves human
level performance. It has the potential of being a useful method for the
assessment of fetal brain biometry in normal and pathological cases, and of
improving routine clinical practice.Comment: 15 pages, 8 figures, presented in CARS 2020, submitted to IJCAR
The role of clinical phenotypes in decisions to limit life-sustaining treatment for very old patients in the ICU
BACKGROUND: Limiting life-sustaining treatment (LST) in the intensive care unit (ICU) by withholding or withdrawing interventional therapies is considered appropriate if there is no expectation of beneficial outcome. Prognostication for very old patients is challenging due to the substantial biological and functional heterogeneity in that group. We have previously identified seven phenotypes in that cohort with distinct patterns of acute and geriatric characteristics. This study investigates the relationship between these phenotypes and decisions to limit LST in the ICU. METHODS: This study is a post hoc analysis of the prospective observational VIP2 study in patients aged 80 years or older admitted to ICUs in 22 countries. The VIP2 study documented demographic, acute and geriatric characteristics as well as organ support and decisions to limit LST in the ICU. Phenotypes were identified by clustering analysis of admission characteristics. Patients who were assigned to one of seven phenotypes (n = 1268) were analysed with regard to limitations of LST. RESULTS: The incidence of decisions to withhold or withdraw LST was 26.5% and 8.1%, respectively. The two phenotypes describing patients with prominent geriatric features and a phenotype representing the oldest old patients with low severity of the critical condition had the largest odds for withholding decisions. The discriminatory performance of logistic regression models in predicting limitations of LST after admission to the ICU was the best after combining phenotype, ventilatory support and country as independent variables. CONCLUSIONS: Clinical phenotypes on ICU admission predict limitations of LST in the context of cultural norms (country). These findings can guide further research into biases and preferences involved in the decision-making about LST. Trial registration Clinical Trials NCT03370692 registered on 12 December 2017
Prognosticating the outcome of intensive care in older patients—a narrative review
Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment
Fetal Brain Tissue Annotation and Segmentation Challenge Results
In-utero fetal MRI is emerging as an important tool in the diagnosis and
analysis of the developing human brain. Automatic segmentation of the
developing fetal brain is a vital step in the quantitative analysis of prenatal
neurodevelopment both in the research and clinical context. However, manual
segmentation of cerebral structures is time-consuming and prone to error and
inter-observer variability. Therefore, we organized the Fetal Tissue Annotation
(FeTA) Challenge in 2021 in order to encourage the development of automatic
segmentation algorithms on an international level. The challenge utilized FeTA
Dataset, an open dataset of fetal brain MRI reconstructions segmented into
seven different tissues (external cerebrospinal fluid, grey matter, white
matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international
teams participated in this challenge, submitting a total of 21 algorithms for
evaluation. In this paper, we provide a detailed analysis of the results from
both a technical and clinical perspective. All participants relied on deep
learning methods, mainly U-Nets, with some variability present in the network
architecture, optimization, and image pre- and post-processing. The majority of
teams used existing medical imaging deep learning frameworks. The main
differences between the submissions were the fine tuning done during training,
and the specific pre- and post-processing steps performed. The challenge
results showed that almost all submissions performed similarly. Four of the top
five teams used ensemble learning methods. However, one team's algorithm
performed significantly superior to the other submissions, and consisted of an
asymmetrical U-Net network architecture. This paper provides a first of its
kind benchmark for future automatic multi-tissue segmentation algorithms for
the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript
submitte
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LITS) organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2016 and International
Conference On Medical Image Computing Computer Assisted Intervention (MICCAI)
2017. Twenty four valid state-of-the-art liver and liver tumor segmentation
algorithms were applied to a set of 131 computed tomography (CT) volumes with
different types of tumor contrast levels (hyper-/hypo-intense), abnormalities
in tissues (metastasectomie) size and varying amount of lesions. The submitted
algorithms have been tested on 70 undisclosed volumes. The dataset is created
in collaboration with seven hospitals and research institutions and manually
reviewed by independent three radiologists. We found that not a single
algorithm performed best for liver and tumors. The best liver segmentation
algorithm achieved a Dice score of 0.96(MICCAI) whereas for tumor segmentation
the best algorithm evaluated at 0.67(ISBI) and 0.70(MICCAI). The LITS image
data and manual annotations continue to be publicly available through an online
evaluation system as an ongoing benchmarking resource.Comment: conferenc
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