The PI-CAI (Prostate Imaging: Cancer AI) challenge led to expert-level
diagnostic algorithms for clinically significant prostate cancer detection. The
algorithms receive biparametric MRI scans as input, which consist of
T2-weighted and diffusion-weighted scans. These scans can be misaligned due to
multiple factors in the scanning process. Image registration can alleviate this
issue by predicting the deformation between the sequences. We investigate the
effect of image registration on the diagnostic performance of AI-based prostate
cancer diagnosis. First, the image registration algorithm, developed in
MeVisLab, is analyzed using a dataset with paired lesion annotations. Second,
the effect on diagnosis is evaluated by comparing case-level cancer diagnosis
performance between using the original dataset, rigidly aligned
diffusion-weighted scans, or deformably aligned diffusion-weighted scans. Rigid
registration showed no improvement. Deformable registration demonstrated a
substantial improvement in lesion overlap (+10% median Dice score) and a
positive yet non-significant improvement in diagnostic performance (+0.3%
AUROC, p=0.18). Our investigation shows that a substantial improvement in
lesion alignment does not directly lead to a significant improvement in
diagnostic performance. Qualitative analysis indicated that jointly developing
image registration methods and diagnostic AI algorithms could enhance
diagnostic accuracy and patient outcomes.

通过使用图像配准算法研究了人工智能诊断算法的影响，发现虽然图像配准显著提高了病变对齐效果，但直接导致诊断准确性的显著提升仍未能得到证实。定性分析表明，同时开发图像配准方法和诊断人工智能算法可以提高诊断准确性和患者预后。

基于变形 MRI 序列配准的人工智能前列腺癌诊断

Deformable MRI Sequence Registration for AI-based Prostate Cancer  Diagnosis

Most state-of-the-art techniques for medical image segmentation rely on
deep-learning models. These models, however, are often trained on
narrowly-defined tasks in a supervised fashion, which requires expensive
labeled datasets. Recent advances in several machine learning domains, such as
natural language generation have demonstrated the feasibility and utility of
building foundation models that can be customized for various downstream tasks
with little to no labeled data. This likely represents a paradigm shift for
medical imaging, where we expect that foundation models may shape the future of
the field. In this paper, we consider a recently developed foundation model for
medical image segmentation, UniverSeg. We conduct an empirical evaluation study
in the context of prostate imaging and compare it against the conventional
approach of training a task-specific segmentation model. Our results and
discussion highlight several important factors that will likely be important in
the development and adoption of foundation models for medical image
segmentation.

对于医学图像分割的基础模型 UniverSeg 进行了实证评估研究，与传统的任务特定分割模型进行了比较，结果和讨论凸显了发展和采用基础模型在医学图像分割领域中的重要因素。