Purpose: Lymph nodes (LNs) in the chest have a tendency to enlarge due to
various pathologies, such as lung cancer or pneumonia. Clinicians routinely
measure nodal size to monitor disease progression, confirm metastatic cancer,
and assess treatment response. However, variations in their shapes and
appearances make it cumbersome to identify LNs, which reside outside of most
organs. Methods: We propose to segment LNs in the mediastinum by leveraging the
anatomical priors of 28 different structures (e.g., lung, trachea etc.)
generated by the public TotalSegmentator tool. The CT volumes from 89 patients
available in the public NIH CT Lymph Node dataset were used to train three 3D
nnUNet models to segment LNs. The public St. Olavs dataset containing 15
patients (out-of-training-distribution) was used to evaluate the segmentation
performance. Results: For the 15 test patients, the 3D cascade nnUNet model
obtained the highest Dice score of 72.2 +- 22.3 for mediastinal LNs with short
axis diameter $\geq$ 8mm and 54.8 +- 23.8 for all LNs respectively. These
results represent an improvement of 10 points over a current approach that was
evaluated on the same test dataset. Conclusion: To our knowledge, we are the
first to harness 28 distinct anatomical priors to segment mediastinal LNs, and
our work can be extended to other nodal zones in the body. The proposed method
has immense potential for improved patient outcomes through the identification
of enlarged nodes in initial staging CT scans.

通过利用公共的 TotalSegmentator 工具生成的 28 个不同结构（如肺部、气管等）的解剖学先验知识，我们提出了一种在纵隔中分割淋巴结的方法，通过三个 3D nnUNet 模型对 89 位患者的 CT 体积进行训练，并使用包含 15 名患者的公共 St. Olavs 数据集对分割性能进行评估。通过识别初始分期 CT 扫描中的肿大淋巴结，我们的工作具有显着的潜力改善患者的预后。

CT 中胸部淋巴结的分割与解剖先验

Segmentation of Mediastinal Lymph Nodes in CT with Anatomical Priors

In surgical oncology, it is challenging for surgeons to identify lymph nodes
and completely resect cancer even with pre-operative imaging systems like PET
and CT, because of the lack of reliable intraoperative visualization tools.
Endoscopic radio-guided cancer detection and resection has recently been
evaluated whereby a novel tethered laparoscopic gamma detector is used to
localize a preoperatively injected radiotracer. This can both enhance the
endoscopic imaging and complement preoperative nuclear imaging data. However,
gamma activity visualization is challenging to present to the operator because
the probe is non-imaging and it does not visibly indicate the activity
origination on the tissue surface. Initial failed attempts used segmentation or
geometric methods, but led to the discovery that it could be resolved by
leveraging high-dimensional image features and probe position information. To
demonstrate the effectiveness of this solution, we designed and implemented a
simple regression network that successfully addressed the problem. To further
validate the proposed solution, we acquired and publicly released two datasets
captured using a custom-designed, portable stereo laparoscope system. Through
intensive experimentation, we demonstrated that our method can successfully and
effectively detect the sensing area, establishing a new performance benchmark.
Code and data are available at
this https URL

使用新型携带式腹腔镜伽马探测器和高维图像特征，成功解决手术肿瘤学中的淋巴结检测问题，并建立了新的性能基准。

微创癌症手术中腹腔镜探头的感知区域检测

Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive  Cancer Surgery

We propose a fully-automated method for accurate and robust detection and
segmentation of potentially cancerous lesions found in the liver and in lymph
nodes. The process is performed in three steps, including organ detection,
lesion detection and lesion segmentation. Our method applies machine learning
techniques such as marginal space learning and convolutional neural networks,
as well as active contour models. The method proves to be robust in its
handling of extremely high lesion diversity. We tested our method on volumetric
computed tomography (CT) images, including 42 volumes containing liver lesions
and 86 volumes containing 595 pathological lymph nodes. Preliminary results
under 10-fold cross validation show that for both the liver lesions and the
lymph nodes, a total detection sensitivity of 0.53 and average Dice score of
$0.71 \pm 0.15$ for segmentation were obtained.

本文提出了一种利用机器学习技术进行肝脏和淋巴结中癌变病灶精确鲁棒检测和分割的全自动方法，验证表明此方法对高度多样化的病灶具有较好的鲁棒性。