Deep Learning Segmentation of Abdominal Organs Across Ultrasound, MRI, and CT: A Scoping Review of Modality-Specific Approaches and the Emerging Role of Foundation Models and LLM-Based Agents.
Sep 2026· Journal of imaging informatics in medicine· 0 citations· 21 references
Medicine
TL;DR
A modality-stratified synthesis shows where ultrasound and the pancreas remain underserved, and indicates that agentic approaches currently augment rather than replace specialist segmentation.
A failure-aware cascaded deep learning framework for automated liver CT segmentation using the publicly available HCC-TACE-Seg dataset is presented and indicates that cascaded localisation and region-of-interest refinement can provide robust liver segmentation while reducing background interference and supporting uncer...
Nisha Joseph, D. Mohan, Jomy George et al.· Journal of Intelligent Decis...· 0 citations
A unified 3D pancreas segmentation framework that applies domain-adversarial learning to 4,604 heterogeneous CT and MRI scans to learn anatomical representations is developed and demonstrated that a unified anatomical representation can support both cross-modality pancreas segmentation and label-efficient downstream tr...
Zi-Liang Hong, Hong-Yi Pan, H. Aktas et al.· 0 citations
The study demonstrates the potential of combining local feature extraction and global contextual learning to achieve more accurate and robust brain tumor segmentation from multimodal MRI images.
Lovedeep Kaur, Parminder Singh, Naveen Dhillon· International Journal of Com...· 0 citations
Kidney CT imaging plays a central role in the urological diagnosis of renal cysts, calculi, solid tumors, and normal renal parenchyma. Although deep learning has shown promise for automated CT-based kidney pathology classification, existing approaches frequently rely on single-architecture models and lack systematic hy...
M. Ağdaş, F. Karahüseyinoğlu· Black Sea Journal of Enginee...· 0 citations
The proposed HDLM-HTA framework greatly improves the current CNN and hybrid deep learning models on various quantitative metrics and is an effective computer-aided liver blood vessels segmentation and liver tumor segmentation method that can be used for liver cancer diagnosis, surgical planning, vascular mapping, treat...
S. S· Journal of Intelligent Decis...· 0 citations
Prostate gland segmentation on magnetic resonance imaging (MRI) is important for prostate cancer
treatment planning, but manual segmentation is time-consuming and subject to inter-reader variability.
Deep learning models may offer automation and efficiency, but boundary uncertainty, tissue heterogeneity,
morphological...
Ryan Karim, Sumedh Sonawane, R. Shiradkar· American Journal of Student...· 0 citations
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