%0 Journal Article
%T Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.
%A Dei D
%A Lambri N
%A Crespi L
%A Brioso RC
%A Loiacono D
%A Clerici E
%A Bellu L
%A De Philippis C
%A Navarria P
%A Bramanti S
%A Carlo-Stella C
%A Rusconi R
%A Reggiori G
%A Tomatis S
%A Scorsetti M
%A Mancosu P
%J Radiol Med
%V 129
%N 3
%D 2024 Mar 2
%M 38308062
%F 6.313
%R 10.1007/s11547-024-01760-8
%X <B>OBJECTIVE: </B>To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.<BR><B>METHODS: </B>Ninety-five TMLI plans optimized in our institute were analyzed. Two commercial DL software were tested for segmenting 18 OARs. An AB model for lymph node CTV (CTV_LN) delineation was built using 20 TMLI patients. The AB model was evaluated on 20 independent patients, and a semiautomatic approach was tested by correcting the automatic contours. The generated OARs and CTV_LN contours were compared to manual contours in terms of topological agreement, dose statistics, and time workload. A clinical decision tree was developed to define a specific contouring strategy for each OAR.<BR><B>RESULTS: </B>The two DL models achieved a median [interquartile range] dice similarity coefficient (DSC) of 0.84 [0.71;0.93] and 0.85 [0.70;0.93] across the OARs. The absolute median Dmean difference between manual and the two DL models was 2.0 [0.7;6.6]% and 2.4 [0.9;7.1]%. The AB model achieved a median DSC of 0.70 [0.66;0.74] for CTV_LN delineation, increasing to 0.94 [0.94;0.95] after manual revision, with minimal Dmean differences. Since September 2022, our institution has implemented DL and AB models for all TMLI patients, reducing from 5 to 2 h the time required to complete the entire segmentation process.<BR><B>CONCLUSIONS: </B>DL models can streamline the TMLI contouring process of OARs. Manual revision is still necessary for lymph node delineation using AB models.