Background: Liver tumors are abnormal growths in the liver that can be either
benign or malignant, with liver cancer being a significant health concern
worldwide. However, there is no dataset for plain scan segmentation of liver
tumors, nor any related algorithms. To fill this gap, we propose Plain Scan
Liver Tumors(PSLT) and YNetr. Methods: A collection of 40 liver tumor plain
scan segmentation datasets was assembled and annotated. Concurrently, we
utilized Dice coefficient as the metric for assessing the segmentation outcomes
produced by YNetr, having advantage of capturing different frequency
information. Results: The YNetr model achieved a Dice coefficient of 62.63% on
the PSLT dataset, surpassing the other publicly available model by an accuracy
margin of 1.22%. Comparative evaluations were conducted against a range of
models including UNet 3+, XNet, UNetr, Swin UNetr, Trans-BTS, COTr, nnUNetv2
(2D), nnUNetv2 (3D fullres), MedNext (2D) and MedNext(3D fullres). Conclusions:
We not only proposed a dataset named PSLT(Plain Scan Liver Tumors), but also
explored a structure called YNetr that utilizes wavelet transform to extract
different frequency information, which having the SOTA in PSLT by experiments.

通过使用 YNetr 结构，利用小波变换提取不同频率信息，在肝肿瘤明确扫描分割方面取得了 PSLT 数据集的 SOTA，得到了 62.63% 的 Dice 系数的准确率。

YNetr：基于普通扫描肝肿瘤 (PSLT) 的双编码器架构

YNetr: Dual-Encoder architecture on Plain Scan Liver Tumors (PSLT)

Retinal vessel segmentation is an indispensable step for automatic detection
of retinal diseases with fundoscopic images. Though many approaches have been
proposed, existing methods tend to miss fine vessels or allow false positives
at terminal branches. Let alone under-segmentation, over-segmentation is also
problematic when quantitative studies need to measure the precise width of
vessels. In this paper, we present a method that generates the precise map of
retinal vessels using generative adversarial training. Our methods achieve dice
coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the
state-of-the-art performance on both datasets.

本研究展示了一种使用生成式对抗训练的方法，用于生成精确的视网膜血管地图，它可以用于自动检测眼底图像中视网膜疾病。

采用生成对抗网络对眼底图像中的视网膜血管进行分割

Retinal Vessel Segmentation in Fundoscopic Images with Generative  Adversarial Networks

Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propose an approach to 3D image segmentation based on a
volumetric, fully convolutional, neural network. Our CNN is trained end-to-end
on MRI volumes depicting prostate, and learns to predict segmentation for the
whole volume at once. We introduce a novel objective function, that we optimise
during training, based on Dice coefficient. In this way we can deal with
situations where there is a strong imbalance between the number of foreground
and background voxels. To cope with the limited number of annotated volumes
available for training, we augment the data applying random non-linear
transformations and histogram matching. We show in our experimental evaluation
that our approach achieves good performances on challenging test data while
requiring only a fraction of the processing time needed by other previous
methods.

本文提出了一种基于全卷积神经网络的三维图像分割方法，通过优化 Dice 系数的目标函数，应对前景和背景体素数量极不平衡的情况，并通过数据增强技术提高数据样本的数量和多样性，实验结果表明该方法在肺部三维图像分割任务中取得了较好的性能。