A volumetric attention(VA) module for 3D medical image segmentation and
detection is proposed. VA attention is inspired by recent advances in video
processing, enables 2.5D networks to leverage context information along the z
direction, and allows the use of pretrained 2D detection models when training
data is limited, as is often the case for medical applications. Its integration
in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver
Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge
winner by 3.9 points and achieving top performance on the LiTS leader board at
the time of paper submission. Detection experiments on the DeepLesion dataset
also show that the addition of VA to existing object detectors enables a 69.1
sensitivity at 0.5 false positive per image, outperforming the best published
results by 6.6 points.

提出了一种 3D 医学图像分割和检测的容积注意力（VA）模块。VA 注意力受到视频处理的最新进展的启发，并使得 2.5D 网络能够利用沿着 z 方向的上下文信息，从而在训练数据有限时允许使用预先训练的 2D 检测模型。其与 Mask R-CNN 的整合显示，在肝肿瘤分割挑战中实现了最优表现，超越了以前的挑战赢家 3.9 分，并在 LiTS 排行榜上达到了最佳成绩。在 DeepLesion 数据集上的检测实验也表明，将 VA 添加到现有的物体检测器中，可以获得 69.1 的灵敏度，每张图像的 0.5 假阳性，超越了已发表的最佳结果 6.6 分。

用于三维医学图像分割和检测的容积式注意力

Volumetric Attention for 3D Medical Image Segmentation and Detection

Acquiring large-scale medical image data, necessary for training machine
learning algorithms, is frequently intractable, due to prohibitive
expert-driven annotation costs. Recent datasets extracted from hospital
archives, e.g., DeepLesion, have begun to address this problem. However, these
are often incompletely or noisily labeled, e.g., DeepLesion leaves over 50% of
its lesions unlabeled. Thus, effective methods to harvest missing annotations
are critical for continued progress in medical image analysis. This is the goal
of our work, where we develop a powerful system to harvest missing lesions from
the DeepLesion dataset at high precision. Accepting the need for some degree of
expert labor to achieve high fidelity, we exploit a small fully-labeled subset
of medical image volumes and use it to intelligently mine annotations from the
remainder. To do this, we chain together a highly sensitive lesion proposal
generator and a very selective lesion proposal classifier. While our framework
is generic, we optimize our performance by proposing a 3D contextual lesion
proposal generator and by using a multi-view multi-scale lesion proposal
classifier. These produce harvested and hard-negative proposals, which we then
re-use to finetune our proposal generator by using a novel hard negative
suppression loss, continuing this process until no extra lesions are found.
Extensive experimental analysis demonstrates that our method can harvest an
additional 9,805 lesions while keeping precision above 90%. To demonstrate the
benefits of our approach, we show that lesion detectors trained on our
harvested lesions can significantly outperform the same variants only trained
on the original annotations, with boost of average precision of 7% to 10%. We
open source our annotations at
this https URL

通过使用小规模的医学图像体积样本来智能地从剩下的样本中挖掘出存在的缺失标注，我们提出了一个强大的系统来从 DeepLesion 数据集中高精度地提取缺失的病变。

病变收割者：大规模迭代挖掘未标记的病变和难负样本

Lesion Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative  Examples at Scale

Lesions characterized by computed tomography (CT) scans, are arguably often
elliptical objects. However, current lesion detection systems are predominantly
adopted from the popular Region Proposal Networks (RPNs) that only propose
bounding boxes without fully leveraging the elliptical geometry of lesions. In
this paper, we present Gaussian Proposal Networks (GPNs), a novel extension to
RPNs, to detect lesion bounding ellipses. Instead of directly regressing the
rotation angle of the ellipse as the common practice, GPN represents bounding
ellipses as 2D Gaussian distributions on the image plain and minimizes the
Kullback-Leibler (KL) divergence between the proposed Gaussian and the ground
truth Gaussian for object localization. We show the KL divergence loss
approximately incarnates the regression loss in the RPN framework when the
rotation angle is 0. Experiments on the DeepLesion dataset show that GPN
significantly outperforms RPN for lesion bounding ellipse detection thanks to
lower localization error. GPN is open sourced at
this https URL

该研究使用 Gaussian Proposal Networks 方法，将计算机断层扫描中的病变检测从矩形检测框推广到椭圆形检测框，证明该方法在 DeepLesion 数据集上能够更准确地检测边界并获得更低的局部化误差。

用高斯提议网络检测病变边界椭圆

Detecting Lesion Bounding Ellipses With Gaussian Proposal Networks

Detecting lesions from computed tomography (CT) scans is an important but
difficult problem because non-lesions and true lesions can appear similar. 3D
context is known to be helpful in this differentiation task. However, existing
end-to-end detection frameworks of convolutional neural networks (CNNs) are
mostly designed for 2D images. In this paper, we propose 3D context enhanced
region-based CNN (3DCE) to incorporate 3D context information efficiently by
aggregating feature maps of 2D images. 3DCE is easy to train and end-to-end in
training and inference. A universal lesion detector is developed to detect all
kinds of lesions in one algorithm using the DeepLesion dataset. Experimental
results on this challenging task prove the effectiveness of 3DCE. We have
released the code of 3DCE in
this https URL

3D context enhanced region-based CNN (3DCE) is proposed for lesion detection from computed tomography (CT) scans, which efficiently incorporates 3D context information by aggregating feature maps of 2D images and is proven effective through experiments on the DeepLesion dataset.