arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
By Xin Ci Wong, Duygu Sarikaya, Kieran Zucker, Marc De Kamps, Nishant Ravikumar
arXiv:2608.28716v1 Announce Type: cross
Abstract: Deep learning segmentation models are often evaluated using geometric metrics such as Dice, HD95, and ASD, yet it remains unclear to what extent impr...
By Savvas Saragiotis, Pieter C. Gort, Lotte J. S. Fleurkens-Ewals, Anna F. van Herwijnen, Marion Tops-Welten, L. D. Kampmeijer, Joost Nederend, Fons van der Sommen
The study introduces Spatial Atlas-Informed Loss Ensembles (SAILS) for whole-body PSMA PET/CT metastasis segmentation, integrating a population metastasis atlas into nnU-Net training via voxel-wise mean and standard deviation maps. SAILS improves lesion detection, notably in high-risk bone regions, achieving higher Dice scores and sensitivity on AutoPET~III and Deep-PSMA datasets compared to baseline methods, though it also increases false-positive volume. The approach eliminates the need for atlas registration at inference time.
By Grant N. Chau, Bashirul A. Biswas, Biratal Wagle, Matthew E. Maeder, Michael S. Leapman, James B. Yu, Indrani Bhattacharya
The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.
By Haobin Liu, Xin Wang
The paper presents a pragmatic segmentation pipeline for brain metastases in the BraTS 2026 Task 1, using a 5‑fold nnU-Net ResEnc‑L ensemble trained for 1,000 epochs on 1,296 four‑modality cases. A rule‑based post‑processing cascade tuned for the lesion‑wise Dice similarity coefficient (LW‑DSC) improves performance, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 on enhancing tumour, tumour core, whole tumour, and resection cavity, respectively. The authors audit each post‑processing stage with a five‑fold out‑of‑fold analysis, confirm two stages as robust, and provide a mechanistic analysis of LW‑DSC, along with thirteen negative results that challenge common intuitions.
A specialized large multimodal model, LLaVA‑NeXT, was fine‑tuned on a curated two‑level curriculum of PET/CT image‑conversation pairs to interpret head and neck cancer scans. In external validation across four institutions, the model achieved high ROUGE and similarity scores and outperformed generalist models such as ChatGPT, with primary tumor classification accuracy of 83.14% internally and 69.03% externally. The study demonstrates that domain‑specific LMMs can provide fast, accurate diagnostic support for PET/CT imaging.
By Haengbok Chung, SunGyu Kim, Joo hyun Lee, Sangjin Bae, Min Jeong Cho, Minseok Suh, Jae Sung Lee