Male infertility contributes substantially to the global infertility burden, and sperm analysis remains central to diagnosis, treatment planning, and assisted reproductive technology. Conventional semen evaluation, however, is labor-intensive, operator-dependent, and limited by inter- and intra-observer variability, motivating the development of objective and reproducible computational approaches.
arXiv:2606. 24390v1 Announce Type: cross Abstract: Standardized assessment of uterine MRI remains challenging due to anatomical variability, observer dependence, and the lack of workflow-integrated automated analysis tools.
By Deepak Bhatia, Saad Ahmad, Smiti Tripathy, Maria Camila Bustos Vivas, Lieselotte Kratzsch, Anika Knupfer, Jordina Aviles Verdera, Susanne Schulz-Heise, Matthias May, Jana Hutter
The study evaluated a pragmatic deep‑learning approach for segmenting acute ischemic stroke lesions on diffusion‑weighted MRI. Using a self‑configured nnU‑Net trained on 1,744 cases and tested on 436, the baseline model achieved a median Dice similarity coefficient of 0.84, outperforming the DeepISLES ensemble, especially for smaller infarcts. The approach required minimal preprocessing and fast inference, suggesting it could streamline clinical stroke imaging workflows.
By Atle Bj{\o}rnerud, Till Schellhorn, Thor H. Skatt{\o}r, Terje Nome, Jon Andr\'e Ottesen, Anne Hege Aamodt, Bradley J MacIntosh
Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine learning still presents important limitations that hamper its clinical utility.
arXiv:2606. 17836v1 Announce Type: cross Abstract: Patient-specific 3D reconstruction of pelvic organ geometry from MRI is important for pelvic floor modeling and downstream patient-specific analysis.
By Hui Wang, Xiaowei Li, Chenxin Zhang, Yifan Feng, Jianwei Zuo, Yumeng Tang, Xiuli Sun, Jianliu Wang, Bing Xie, Jiajia Luo
The study evaluates lesion‑guided region‑of‑interest (ROI) deep learning for ovarian ultrasound classification, comparing it to global image, lesion contour, and contour‑based radiomics approaches across two public datasets. Using four deep‑learning architectures, the lesion‑guided ROI strategy achieved the highest accuracy (93.10% on MMOTU and 97.56% on OUD) with an AUC of 0.99, while requiring less annotation effort than contour‑based methods.
By Mehran Ahmad, Ali Abbasian Ardakani, Afshin Mohammadi, Alisa Mohebbi, Gernot Kronreif, Sepideh Hatamikia