Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Conclusion and References

by Reinforcement Technology AdvancementsJune 12th, 2024

Too Long; Didn't Read

This study performs comprehensive evaluation of four neural network architectures for lymphoma lesion segmentation from PET/CT images.

featured image - Deep Neural Networks to Detect and Quantify Lymphoma Lesions: Conclusion and References

Authors:

(1) Shadab Ahamed, University of British Columbia, Vancouver, BC, Canada, BC Cancer Research Institute, Vancouver, BC, Canada. He was also a Mitacs Accelerate Fellow (May 2022 - April 2023) with Microsoft AI for Good Lab, Redmond, WA, USA (e-mail: [email protected]);

(2) Yixi Xu, Microsoft AI for Good Lab, Redmond, WA, USA;

(3) Claire Gowdy, BC Children’s Hospital, Vancouver, BC, Canada;

(4) Joo H. O, St. Mary’s Hospital, Seoul, Republic of Korea;

(5) Ingrid Bloise, BC Cancer, Vancouver, BC, Canada;

(6) Don Wilson, BC Cancer, Vancouver, BC, Canada;

(7) Patrick Martineau, BC Cancer, Vancouver, BC, Canada;

(8) Franc¸ois Benard, BC Cancer, Vancouver, BC, Canada;

(9) Fereshteh Yousefirizi, BC Cancer Research Institute, Vancouver, BC, Canada;

(10) Rahul Dodhia, Microsoft AI for Good Lab, Redmond, WA, USA;

(11) Juan M. Lavista, Microsoft AI for Good Lab, Redmond, WA, USA;

(12) William B. Weeks, Microsoft AI for Good Lab, Redmond, WA, USA;

(13) Carlos F. Uribe, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada;

(14) Arman Rahmim, BC Cancer Research Institute, Vancouver, BC, Canada, and University of British Columbia, Vancouver, BC, Canada.

Table of Links

VI. CONCLUSION

In this study, we assessed various neural network architectures for automating lymphoma lesion segmentation in PET/CT images across multiple datasets. We examined the reproducibility of lesion measures, revealing differences among networks, highlighting their suitability for specific clinical uses. Additionally, we introduced three lesion detection criteria to assess network performance at a per-lesion level, emphasizing their clinical relevance. Lastly, we discussed challenges related to ground truth consistency and stressed the importance of having well-defined protocol for segmentation. This work provides valuable insights into deep learning’s potentials and limitations in lymphoma lesion segmentation and emphasizes the need for standardized annotation practices to enhance research validity and clinical applications.

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