Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans.
- 作者列表："Boers TGW","Hu Y","Gibson E","Barratt DC","Bonmati E","Krdzalic J","van der Heijden F","Hermans JJ","Huisman HJ
:The increasing incidence of pancreatic cancer will make it the second deadliest cancer in 2030. Imaging based early diagnosis and image guided treatment are emerging potential solutions. Artificial intelligence (AI) can help provide and improve widespread diagnostic expertise and accurate interventional image interpretation. Accurate segmentation of the pancreas is essential to create annotated data sets to train AI, and for computer assisted interventional guidance. Automated deep learning segmentation performance in pancreas computed tomography (CT) imaging is low due to poor grey value contrast and complex anatomy. A good solution seemed a recent interactive deep learning segmentation framework for brain CT that helped strongly improve initial automated segmentation with minimal user input. This method yielded no satisfactory results for pancreas CT, possibly due to a sub-optimal neural network architecture. We hypothesize that a state-of-the-art U-net neural network architecture is better because it can produce a better initial segmentation and is likely to be extended to work in a similar interactive approach. We implemented the existing interactive method, iFCN, and developed an interactive version of U-net method we call iUnet. The iUnet is fully trained to produce the best possible initial segmentation. In interactive mode it is additionally trained on a partial set of layers on user generated scribbles. We compare initial segmentation performance of iFCN and iUnet on a 100CT dataset using dice similarity coefficient analysis. Secondly, we assessed the performance gain in interactive use with three observers on segmentation quality and time. Average automated baseline performance was 78% (iUnet) versus 72% (FCN). Manual and semi-automatic segmentation performance was: 87% in 15 min. for manual, and 86% in 8 min. for iUNet. We conclude that iUnet provides a better baseline than iFCN and can reach expert manual performance significantly faster than manual segmentation in case of pancreas CT. Our novel iUnet architecture is modality and organ agnostic and can be a potential novel solution for semi-automatic medical imaging segmentation in general.
: 胰腺癌发病率的增加将使其成为2030年第二致命的癌症。基于成像的早期诊断和图像引导治疗是新兴的潜在解决方案。人工智能 (AI) 可以帮助提供和改善广泛的诊断专业知识和准确的介入图像解释。胰腺的准确分割对于创建注释数据集以训练AI以及计算机辅助介入指导至关重要。由于较差的灰度值对比度和复杂的解剖结构，胰腺计算机断层扫描 (CT) 成像中的自动化深度学习分割性能较低。一个很好的解决方案似乎是最近的脑ct交互式深度学习分割框架，它有助于以最小的用户输入强烈改善初始自动分割。该方法对于胰腺CT没有产生令人满意的结果，可能是由于次优的神经网络结构。我们假设最先进的U-net神经网络架构更好，因为它可以产生更好的初始分割，并且可能被扩展到类似的交互式方法中工作。我们实现了现有的交互式方法iFCN，并开发了一个我们称之为iUnet的交互式版本的U-net方法。iUnet被完全训练以产生最佳的可能的初始分割。在交互模式下，它在用户生成的涂鸦上额外训练部分层集。我们使用dice相似性系数分析在100CT数据集上比较iFCN和iUnet的初始分割性能。其次，我们评估了与三个观察者在分割质量和时间上的交互使用的性能增益。平均自动化基线性能为78% (iUnet) 对72% (FCN)。手动和半自动分割性能为: 15分钟内87%。对于手动，86% 在8 min min。对于iUNet。我们的结论是，iUnet提供了比iFCN更好的基线，并且在胰腺CT的情况下，可以比手动分割更快地达到专家手动性能。我们的新型iUnet架构是模态和器官不可知的，通常可以成为半自动医学成像分割的潜在新型解决方案。
METHODS:OBJECTIVES:The aim was to evaluate the image quality and sensitivity to artifacts of compressed sensing (CS) acceleration technique, applied to 3D or breath-hold sequences in different clinical applications from brain to knee. METHODS:CS with an acceleration from 30 to 60% and conventional MRI sequences were performed in 10 different applications in 107 patients, leading to 120 comparisons. Readers were blinded to the technique for quantitative (contrast-to-noise ratio or functional measurements for cardiac cine) and qualitative (image quality, artifacts, diagnostic findings, and preference) image analyses. RESULTS:No statistically significant difference in image quality or artifacts was found for each sequence except for the cardiac cine CS for one of both readers and for the wrist 3D proton density (PD)-weighted CS sequence which showed less motion artifacts due to the reduced acquisition time. The contrast-to-noise ratio was lower for the elbow CS sequence but not statistically different in all other applications. Diagnostic findings were similar between conventional and CS sequence for all the comparisons except for four cases where motion artifacts corrupted either the conventional or the CS sequence. CONCLUSIONS:The evaluated CS sequences are ready to be used in clinical daily practice except for the elbow application which requires a lower acceleration. The CS factor should be tuned for each organ and sequence to obtain good image quality. It leads to 30% to 60% acceleration in the applications evaluated in this study which has a significant impact on clinical workflow. KEY POINTS:• Clinical implementation of compressed sensing (CS) reduced scan times of at least 30% with only minor penalty in image quality and no change in diagnostic findings. • The CS acceleration factor has to be tuned separately for each organ and sequence to guarantee similar image quality than conventional acquisition. • At least 30% and up to 60% acceleration is feasible in specific sequences in clinical routine.
METHODS:BACKGROUND:The main surgical techniques for spontaneous basal ganglia hemorrhage include stereotactic aspiration, endoscopic aspiration, and craniotomy. However, credible evidence is still needed to validate the effect of these techniques. OBJECTIVE:To explore the long-term outcomes of the three surgical techniques in the treatment of spontaneous basal ganglia hemorrhage. METHODS:Five hundred and sixteen patients with spontaneous basal ganglia hemorrhage who received stereotactic aspiration, endoscopic aspiration, or craniotomy were reviewed retrospectively. Six-month mortality and the modified Rankin Scale score were the primary and secondary outcomes, respectively. A multivariate logistic regression model was used to assess the effects of different surgical techniques on patient outcomes. RESULTS:For the entire cohort, the 6-month mortality in the endoscopic aspiration group was significantly lower than that in the stereotactic aspiration group (odds ratio (OR) 4.280, 95% CI 2.186 to 8.380); the 6-month mortality in the endoscopic aspiration group was lower than that in the craniotomy group, but the difference was not significant (OR=1.930, 95% CI 0.835 to 4.465). A further subgroup analysis was stratified by hematoma volume. The mortality in the endoscopic aspiration group was significantly lower than in the stereotactic aspiration group in the medium (≥40-<80 mL) (OR=2.438, 95% CI 1.101 to 5.402) and large hematoma subgroup (≥80 mL) (OR=66.532, 95% CI 6.345 to 697.675). Compared with the endoscopic aspiration group, a trend towards increased mortality was observed in the large hematoma subgroup of the craniotomy group (OR=8.721, 95% CI 0.933 to 81.551). CONCLUSION:Endoscopic aspiration can decrease the 6-month mortality of spontaneous basal ganglia hemorrhage, especially in patients with a hematoma volume ≥40 mL.
METHODS:OBJECTIVE:The primary purpose of this study was to evaluate the effectiveness of a three-dimensional (3D) software tool (smart planes) for displaying fetal brain planes, and the secondary purpose was to evaluate its accuracy in performing automatic measurements. MATERIAL AND METHODS:This prospective study included singleton fetuses with a gestational age (GA) greater than 18 weeks. Transabdominal two-dimensional ultrasound (2DUS) and 3D smart planes images were respectively used to obtain the basic planes of the fetal brain, with five parameters measured. The images, by either two-dimensional (2D) manual or 3D automatic operation, were reviewed by two experienced sonographers. The agreements between two measurements were analyzed. RESULTS:A total of 226 cases were included. The rates of successful detection by automatic display were as high as 80%. There was substantial agreement between the measurements of the biparietal diameter, head circumference and transcerebellar diameter, but poor agreement between the measurements of cisterna magna and lateral ventricle width. CONCLUSIONS:Smart Planes might be valuable for the rapid evaluation of fetal brain, because it simplifies the evaluation process. However, the technology requires improvement. In addition, this technology cannot replace the conventional manual US scans; it can only be used as an additional approach.